diff --git a/docs/dust.rst b/docs/dust.rst
index 7016e67..9d293d4 100644
--- a/docs/dust.rst
+++ b/docs/dust.rst
@@ -5,6 +5,17 @@ dust
 
 .. autofunction:: load
 
+.. autofunction:: suggest_opacity_sampling
+
+.. autoclass:: IsotropicDust
+   :show-inheritance:
+
+.. autoclass:: HenyeyGreensteinDust
+   :show-inheritance:
+
+.. autoclass:: GeneralDust
+   :show-inheritance:
+
 .. autoclass:: Dust
    :show-inheritance:
 
@@ -17,7 +28,6 @@ dust
       ~Dust.test_model
       ~Dust.run_dust_simulation
       ~Dust.plot_opacity_model
-      ~Dust.plot_pmo_model
       ~Dust.plot_random_nu_model
       ~Dust.plot_ml_step
       ~Dust.save
@@ -29,7 +39,6 @@ dust
    .. automethod:: test_model
    .. automethod:: run_dust_simulation
    .. automethod:: plot_opacity_model
-   .. automethod:: plot_pmo_model
    .. automethod:: plot_random_nu_model
    .. automethod:: plot_ml_step
    .. automethod:: save
\ No newline at end of file
diff --git a/docs/dustcreation.rst b/docs/dustcreation.rst
index 48d5317..18ee0c1 100644
--- a/docs/dustcreation.rst
+++ b/docs/dustcreation.rst
@@ -2,16 +2,28 @@ Creating a dust model
 =====================
 
 Before running a radiative transfer simulation, you need to create a dust model that defines the optical properties of 
-the dust grains in your simulation. Pinball-rt provides a Dust class that allows you to create and manipulate dust 
-models. To set up a dust model, the absorption and scattering opacities as a function of wavelength and grain size 
-distribution parameters (maximum dust grain size and size distribution power-law index) are needed. At present, these 
-must be obtained from external sources and provided to pinball-rt. Here we'll use simple power-law prescription, but in
-practice you would typically use opacities derived from laboratory measurements or Mie theory calculations. Note that 
-the opacities should include astropy units to ensure that there is no ambiguity.
+the dust grains in your simulation. Pinball-rt provides a :class:`~pinballrt.dust.Dust` class that allows you to create 
+and manipulate dust models. In practice, there are three more specific dust models available: 
+:class:`~pinballrt.dust.IsotropicDust`, :class:`~pinballrt.dust.HenyeyGreensteinDust`, and 
+:class:`~pinballrt.dust.GeneralDust` that inherit from :class:`~pinballrt.dust.Dust` and enable more specific control 
+over dust scattering properties. To set up a dust model, the absorption and scattering opacities as a function of wavelength 
+and grain size distribution parameters (maximum dust grain size, size distribution power-law index, and sub-species 
+relative abundances) are needed. At present, these must be obtained from external sources and provided to pinball-rt. 
+Here we'll use simple power-law prescription, but in practice you would typically use opacities derived from laboratory 
+measurements or Mie theory calculations. Note that the opacities should include astropy units to ensure that there is no 
+ambiguity.
+
+To enable maximum flexibility, opacities do not need to be provided on a regular grid. Instead, the opacities should be 
+provided at some number (nsamples) of points in the N-dimensional parameter space defined by relevant inputs for determining
+the opacity (maximum grain size, size distribution power-law index, sub-species abundances), and for each sample they
+should be provided at a specified set of wavelengths. The :class:`~pinballrt.dust.Dust` class will then use machine learning 
+to learn the opacities at any point in the parameter space during the simulation. A helper-function, 
+:func:`~pinballrt.dust.suggest_opacity_sampling` is provided to help provide efficient sampling of the parameter space, 
+but the user is free to provide any set of samples they choose.
 
 .. code-block:: python
 
-   from pinballrt.dust import Dust
+   from pinballrt.dust import IsotropicDust, suggest_opacity_sampling
    import numpy as np
    import astropy.units as u
 
@@ -19,49 +31,52 @@ the opacities should include astropy units to ensure that there is no ambiguity.
    wavelengths = np.logspace(-1, 4, 100) * u.micron
 
    # Define the dust size distribution properties.
-   amax = np.logspace(-4., 1., 60.) * u.cm
-   p = np.logspace(2.5, 4.5, 11)
+   samples = suggest_opacity_sampling(100, amax_range=(1.*u.micron, 10*u.cm), p_range=(2.5, 4.5))
 
-   p, amax, wavelengths = np.meshgrid(p, amax, wavelengths, indexing='ij')
+   # Expand the samples to have a wavelength dimension (nproperties, nsamples, nwavelengths).
+   samples = np.moveaxis(np.repeat(np.expand_dims(samples, 1), wavelengths.size, axis=1), -1, 0)
+
+   amax = samples[1] * u.cm
+   p = samples[0]
+
+   # Expand the wavelengths to have a sample dimension (nsamples, nwavelengths).
+   wavelengths = np.repeat(np.expand_dims(wavelengths, axis=0), max(samples.shape[1], 1), axis=0)
 
    # Define the absorption and scattering opacities (in cm^2/g).
    power_law_index = (-2. / (1 + np.exp(-p/3.5 * (-1.5 - np.log10(amax.to(u.cm))))))
    kappa_abs = 1.0 * (wavelengths.to(u.micron)/100.0)**power_law_index * u.cm**2 / u.g
    kappa_scat = 0.5 * (wavelengths.to(u.micron)/100.0)**power_law_index * u.cm**2 / u.g
 
-   # Create the Dust object.
-   dust = Dust(lam=wavelengths[0,0,:], 
-               amax=amax[0,:,0], 
-               p=p[:,0,0], 
-               kabs=kappa_abs, 
-               ksca=kappa_scat)
+   # Create the IsotropicDust object.
+   dust = IsotropicDust(lam=wavelengths[0,:], 
+                         amax=amax[:,0], 
+                         p=p[:,0], 
+                         kabs=kappa_abs, 
+                         ksca=kappa_scat)
 
-This creates a Dust object with the specified opacities, however a few additional steps are needed before the dust model can be used in a 
-radiative transfer simulation. The Dust object uses a machine learning model to produce opacity values during the simulation, as well as to 
+This creates an :class:`~pinballrt.dust.IsotropicDust` object with the specified opacities, however a few additional steps are needed before the dust model can be used in a 
+radiative transfer simulation. The :class:`~pinballrt.dust.IsotropicDust` object uses a machine learning model to produce opacity values during the simulation, as well as to 
 randomnly sample photon frequencies emitted by dust grains during the simulation, but these models need to be trained first. To set up the 
-training, we use the `learn` method. For example, to set up the model to learn the absorption opacity:
+training, we use the :meth:`~pinballrt.dust.Dust.learn` method. For example, to set up the model to learn the absorption opacity:
 
 .. code-block:: python
 
    # Set up the training parameters.
-   dust.learn(
-       model="kabs",
-       nsamples=100000,
-       test_fraction=0.1,
-       val_fraction=0.1,
-       hidden_units=(48, 48, 48),
-   )
+   dust.learn(model="kabs",
+              test_fraction=0.1,
+              val_fraction=0.1,
+              hidden_units=(48, 48, 48))
 
 This example sets up a simple neural network model with three hidden layers of 48 units each to learn the dust absorption opacity. The training 
-will use 100,000 samples, with 10% of the samples reserved for testing and another 10% for validation. Once the training parameters are set up, 
-we can train the model using the `fit` method:
+will use the kabs samples provided above, which had 100 samples across dust properties at 100 wavelengths for 10,000 total samples, with 10% of 
+the samples reserved for testing and another 10% for validation. Once the training parameters are set up, we can train the model using the :meth:`~pinballrt.dust.Dust.fit` method:
 
 .. code-block:: python
 
    # Train the model.
    dust.fit(epochs=50)
 
-Finally, we can evaluate the trained model using the `test_model` method:
+Finally, we can evaluate the trained model using the :meth:`~pinballrt.dust.Dust.test_model` method:
 
 .. code-block:: python
 
@@ -74,17 +89,17 @@ a simulation. In short:
 .. code-block:: python
 
    for model in ["ksca", "pmo", "random_nu"]:
-      if model in ["kabs", "ksca"]:
-         d.learn(model=model, nsamples=100000, hidden_units=(16,)*6, overwrite=True)
-      else:
-         d.learn(model=model, nsamples=10000, hidden_units=(16,)*6, overwrite=True)
+       if model in ["kabs", "ksca"]:
+          d.learn(model=model, hidden_units=(16,)*6, overwrite=True)
+       else:
+          d.learn(model=model, hidden_units=(48,)*3, overwrite=True)
 
-   d.fit(epochs=300, batch_size=10000)
-   d.test_model(plot=True)
+       d.fit(epochs=300, batch_size=1000)
+       d.test_model(plot=True)
 
 This will further create models to produce the scattering opacity, planck mean opacity, and random frequencies sampled from the dust emission spectrum. 
-Having to train the dust model before every simulation would be inefficient, so once the model is trained it can be saved to a file using the `save` method, 
-and later loaded using the `load` function:
+Having to train the dust model before every simulation would be inefficient, so once the model is trained it can be saved to a file using the :meth:`~pinballrt.dust.Dust.save` method, 
+and later loaded using the :func:`~pinballrt.dust.load` function:
 
 .. code-block:: python
 
@@ -95,7 +110,7 @@ and later loaded using the `load` function:
    from pinballrt.dust import load
    dust = load("dust_model.dst")
 
-pinball-rt will search the default directory as well as the ~/.pinball-rt/data/dust/ directory for dust model files when loading. Additionally, 
+pinball-rt will search the default directory as well as the ``~/.pinball-rt/data/dust/`` directory for dust model files when loading. Additionally, 
 pinball-rt provides a pre-trained dust model that can be used directly without needing to train a new model from scratch:
 
 .. code-block:: python
@@ -105,13 +120,59 @@ pinball-rt provides a pre-trained dust model that can be used directly without n
    # Load the pre-trained dust model.
    dust = load("yso.dst")
 
+Creating a Henyey-Greenstein dust model follows the same process as above, but with the addition of needing to train a model to produce the scattering asymmetry parameter (g) as a function of wavelength and dust properties:
+
+.. code-block:: python
+
+   from pinballrt.dust import HenyeyGreensteinDust
+
+   g = np.tanh(p - np.log10(wavelengths.to(u.micron).value))
+
+   # Create the Henyey-Greenstein dust model.
+   dust = HenyeyGreensteinDust(lam=wavelengths[0,:], 
+                               amax=amax[:,0], 
+                               p=p[:,0], 
+                               kabs=kappa_abs, 
+                               ksca=kappa_scat,
+                               g=g)
+
+   d.learn(model="g", hidden_units=(16,)*6, overwrite=True)
+
+   d.fit(epochs=300, batch_size=1000)
+   d.test_model(plot=True)
+
+Similarly, the most general dust model, :class:`~pinballrt.dust.GeneralDust`, follows the same process but with the addition of needing to train a model to produce the scattering phase function as a function of wavelength, scattering angle and dust properties, and additionally to randomly sample scattering angles during the simulation:
+
+.. code-block:: python
+
+   from pinballrt.dust import GeneralDust
+
+   g = np.repeat(np.expand_dims(np.tanh(p - np.log10(wavelengths.to(u.micron).value)), axis=-1), 5, axis=-1)
+   theta = np.tile(np.expand_dims(np.linspace(0, 180., 5), axis=(0,1)), (10 if len(dims) > 0 else 1, 10, 1)) * u.deg
+   scattering_phase_function = (1 - g**2) / (4 * np.pi * (1 + g**2 - 2*g*np.cos(theta.to(u.rad).value))**(3/2))
+
+   # Create the General dust model.
+   dust = GeneralDust(lam=wavelengths[0,:], 
+                      amax=amax[:,0], 
+                      p=p[:,0], 
+                      kabs=kappa_abs, 
+                      ksca=kappa_scat,
+                      scattering_phase_function=scattering_phase_function
+                      theta=theta[0,0,:])
+
+   for model in ["scattering_phase_function", "random_direction"]:
+       d.learn(model=model, hidden_units=(16,)*6, overwrite=True)
+
+       d.fit(epochs=300, batch_size=1000)
+       d.test_model(plot=True)
+
 Learning to step through high optical depth regions
 ---------------------------------------------------
 
 Historically, radiative transfer simulations in regions of high optical depth have been challenging due to the large number of interactions photons undergo 
 before escaping. Pinball-rt addresses this issue by implementing a machine learning approach that allows photons to "step through" high optical depth regions 
 more efficiently. This is achieved by training a model to predict the output properties of photons traveling through regions of known input optical depths. 
-To set this up, we can again use the `learn` method of the Dust class:
+To set this up, we can again use the :meth:`~pinballrt.dust.Dust.learn` method of the Dust class:
 
 .. code-block:: python
 
diff --git a/examples/dust-demo-diana.ipynb b/examples/dust-demo-diana.ipynb
new file mode 100644
index 0000000..6edf5bf
--- /dev/null
+++ b/examples/dust-demo-diana.ipynb
@@ -0,0 +1,379 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "461d2258",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pinballrt.dust import IsotropicDust, HenyeyGreensteinDust, GeneralDust, load, suggest_opacity_sampling\n",
+    "from astropy.modeling import models\n",
+    "import astropy.units as u\n",
+    "import numpy as np\n",
+    "\n",
+    "import subprocess\n",
+    "\n",
+    "import astropy.io.fits as fits\n",
+    "\n",
+    "import tempfile\n",
+    "from multiprocess import Pool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "1a91d214",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def worker(sample):\n",
+    "    p = sample[0]\n",
+    "    a = sample[1] * u.cm\n",
+    "    Vcarbon = sample[2]\n",
+    "    porosity = sample[3]\n",
+    "\n",
+    "    with  tempfile.TemporaryDirectory() as tmpdirname:\n",
+    "        #print(f\"/Users/psheehan/pinball-rt/examples/optool/OpacityTool -amax {a.to(u.micron).value} -apow {p} -Vcarbon {Vcarbon} -porosity {porosity} -nlam 1000 -lmax 10000\")\n",
+    "        subprocess.run(f\"/Users/psheehan/pinball-rt/examples/optool/OpacityTool -amax {a.to(u.micron).value} -apow {p} -Vcarbon {Vcarbon} -porosity {porosity} -nlam 1000 -lmax 10000\".split(), capture_output=True, cwd=tmpdirname)\n",
+    "\n",
+    "        data = np.loadtxt(tmpdirname+\"/particle.dat\")\n",
+    "\n",
+    "        lam = data[:,0] * u.micron\n",
+    "        kabs = data[:,1]\n",
+    "        ksca = data[:,2]\n",
+    "        g = data[:,3]\n",
+    "\n",
+    "        data = fits.open(tmpdirname+\"/particle.fits\")\n",
+    "\n",
+    "        scattering_phase = data[1].data[:,0,:].T\n",
+    "\n",
+    "    return lam, kabs, ksca, g, scattering_phase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b63d1d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = suggest_opacity_sampling(600, p_range=(2.5, 4.5), amax_range=(1.*u.micron, 1.*u.cm), n_dust_subspecies=3)\n",
+    "\n",
+    "with Pool(6) as pool:\n",
+    "    results = pool.map(worker, samples)\n",
+    "#results = [worker(sample) for sample in samples]\n",
+    "\n",
+    "p = samples[:,0]\n",
+    "amax = samples[:,1] * u.cm\n",
+    "Vcarbon = samples[:,2]\n",
+    "porosity = samples[:,3]\n",
+    "\n",
+    "lam = results[0][0]  # all results should have the same lam, so we can just take it from the first one\n",
+    "kabs = np.array([r[1] for r in results]) * u.cm**2 / u.g\n",
+    "ksca = np.array([r[2] for r in results]) * u.cm**2 / u.g\n",
+    "g = np.array([r[3] for r in results])\n",
+    "scattering_phase = np.array([r[4] for r in results])\n",
+    "\n",
+    "theta = np.linspace(0, 180, scattering_phase.shape[-1]) * u.deg\n",
+    "\n",
+    "np.savez(\"dust_data.npz\", \n",
+    "         kabs=kabs, \n",
+    "         ksca=ksca, \n",
+    "         g=g, \n",
+    "         scattering_phase=scattering_phase, \n",
+    "         theta=theta, \n",
+    "         amax=amax, \n",
+    "         p=p, \n",
+    "         lam=lam, \n",
+    "         Vcarbon=Vcarbon, \n",
+    "         porosity=porosity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96a02ab2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.load(\"dust_data.npz\")\n",
+    "\n",
+    "kabs = data[\"kabs\"] * u.cm**2 / u.g\n",
+    "ksca = data[\"ksca\"] * u.cm**2 / u.g\n",
+    "g = data[\"g\"]\n",
+    "scattering_phase = data[\"scattering_phase\"]\n",
+    "theta = data[\"theta\"] * u.deg\n",
+    "amax = data[\"amax\"] * u.cm\n",
+    "p = data[\"p\"]\n",
+    "lam = data[\"lam\"] * u.micron\n",
+    "Vcarbon = data[\"Vcarbon\"]\n",
+    "porosity = data[\"porosity\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "89f9421c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warp CUDA warning: Could not find or load the NVIDIA CUDA driver. GPU execution will not be available.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"d = HenyeyGreensteinDust(lam=lam, \n",
+    "         kabs=kabs, \n",
+    "         ksca=ksca,\n",
+    "         g=g,\n",
+    "         amax=amax, \n",
+    "         p=p)\"\"\"\n",
+    "\n",
+    "p, amax = np.meshgrid(p, amax, indexing='ij')\n",
+    "\n",
+    "p = p.flatten()\n",
+    "amax = amax.flatten()\n",
+    "\n",
+    "d = GeneralDust(lam=lam,\n",
+    "                kabs=kabs.reshape(-1, lam.size),\n",
+    "                ksca=ksca.reshape(-1, lam.size),\n",
+    "                scattering_phase_function=scattering_phase.reshape(-1, lam.size, theta.size),\n",
+    "                theta=theta.to(u.radian),\n",
+    "                amax=amax,\n",
+    "                p=p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "adf95f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*******************************\n",
+      "random_direction\n",
+      "*******************************\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /workspaces/pinball-rt/examples/random_direction_lightning_logs exists and is not empty.\n",
+      "\n",
+      "  | Name  | Type                 | Params | Mode \n",
+      "-------------------------------------------------------\n",
+      "0 | model | MultiLayerPerceptron | 12.0 K | train\n",
+      "-------------------------------------------------------\n",
+      "12.0 K    Trainable params\n",
+      "0         Non-trainable params\n",
+      "12.0 K    Total params\n",
+      "0.048     Total estimated model params size (MB)\n",
+      "16        Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|██████████| 206/206 [00:34<00:00,  5.97it/s, v_num=18, train_loss=0.00748, valid_loss=0.00676]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|██████████| 206/206 [00:35<00:00,  5.88it/s, v_num=18, train_loss=0.00748, valid_loss=0.00676]\n",
+      "Testing DataLoader 0: 100%|██████████| 29/29 [00:03<00:00,  8.03it/s]\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "       Test metric             DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "        test_loss           0.00679434509947896\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Predicting DataLoader 0: 100%|██████████| 29/29 [00:03<00:00,  7.88it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x1100 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#for model in [\"kabs\", \"ksca\", \"pmo\", \"g\", \"random_nu\"]:\n",
+    "#for model in [\"kabs\", \"ksca\", \"pmo\", \"scattering_phase_function\", \"random_nu\", \"random_direction\"]:\n",
+    "#for model in [\"scattering_phase_function\"]:\n",
+    "for model in [\"random_direction\"]:\n",
+    "    print(\"*******************************\")\n",
+    "    print(model)\n",
+    "    print(\"*******************************\")\n",
+    "\n",
+    "    if model in [\"random_direction\"]:\n",
+    "        hidden_units = (48,)*6\n",
+    "    elif model in [\"random_nu\"]:\n",
+    "        hidden_units = (48,)*3\n",
+    "    else:\n",
+    "        hidden_units = (16,)*6\n",
+    "        \n",
+    "    d.learn(model=model, nsamples=1000, hidden_units=hidden_units, overwrite=True)\n",
+    "\n",
+    "    d.fit(epochs=5, batch_size=10000)\n",
+    "    d.test_model(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2c739068",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.save(\"diana.general.dst\")\n",
+    "#d.save(\"diana.hg.dst\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46da9525",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from schwimmbad import MultiPool\n",
+    "from numpy.random import SeedSequence, seed, randint, set_state\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "pool = MultiPool(initializer=tqdm.set_lock, initargs=(tqdm.get_lock(),))\n",
+    "\n",
+    "def run_dust_simulation(x):\n",
+    "    s, position = x\n",
+    "    seed(s.generate_state(1)[0])\n",
+    "    df = d.run_dust_simulation(nphotons=1000000, tau_range=(3., 30.), nu_range=(d.nu.min()*10000, d.nu.max()), position=position)\n",
+    "    return df\n",
+    "\n",
+    "result = pool.map(run_dust_simulation, list(zip(SeedSequence(12345).spawn(pool.size), range(pool.size))))\n",
+    "\n",
+    "df = pd.concat(result, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1ee5dd93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"sim_results.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9c70d62b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'd' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43md\u001b[49m.learn(model=\u001b[33m'\u001b[39m\u001b[33mml_step\u001b[39m\u001b[33m'\u001b[39m, nsamples=\u001b[32m16000000\u001b[39m, hidden_units=((\u001b[32m128\u001b[39m, \u001b[32m128\u001b[39m),)*\u001b[32m6\u001b[39m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'd' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "d.learn(model='ml_step', nsamples=16000000, hidden_units=((128, 128),)*6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4eefa65c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.fit(epochs=100, batch_size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d30c954d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.test_model(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51af9132",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import astropy.units as u\n",
+    "d.plot_specific_ml_step(tau=1.5, temperature=600*u.K, nu=3e5*u.GHz, nsamples=100000, plot_columns=np.array([\"log10_nu\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63f9bd1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.save(\"amax.dst\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pinball",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/dust-demo-diana_wice.ipynb b/examples/dust-demo-diana_wice.ipynb
new file mode 100644
index 0000000..c316a58
--- /dev/null
+++ b/examples/dust-demo-diana_wice.ipynb
@@ -0,0 +1,11469 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "461d2258",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pinballrt.dust import Dust, load, suggest_opacity_sampling\n",
+    "import astropy.units as u\n",
+    "import numpy as np\n",
+    "\n",
+    "from scipy.interpolate import interp1d, interpn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "89f9421c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.load(\"../pinballrt/tests/data/diana_wice.npz\")\n",
+    "\n",
+    "nsamples = 4000\n",
+    "\n",
+    "samples = suggest_opacity_sampling(nsamples, p_range=(2.5, 4.5), amax_range=(1.*u.micron, 10.*u.cm), n_dust_subspecies=1)\n",
+    "\n",
+    "p = samples[:,0].copy()\n",
+    "amax = samples[:,1].copy() * u.cm\n",
+    "\n",
+    "samples[:,1] = np.log10(samples[:,1])\n",
+    "\n",
+    "lam = np.logspace(np.log10(data[\"lam\"].min())+0.0001, np.log10(data[\"lam\"].max())-0.0001, 300)*u.cm\n",
+    "\n",
+    "samples = np.repeat(np.expand_dims(samples, axis=1), lam.size, axis=1)\n",
+    "\n",
+    "log10_lam = np.repeat(np.expand_dims(np.log10(lam.to(u.cm).value), axis=(0,-1)), samples.shape[0], axis=0)\n",
+    "\n",
+    "samples = np.concatenate((samples, log10_lam), axis=-1).reshape((-1, 3))\n",
+    "\n",
+    "kabs = 10.**interpn((data[\"p\"], np.log10(data[\"amax\"]), np.log10(data[\"lam\"])), np.log10(data[\"kabs\"]), samples, method=\"linear\")\n",
+    "ksca = 10.**interpn((data[\"p\"], np.log10(data[\"amax\"]), np.log10(data[\"lam\"])), np.log10(data[\"ksca\"]), samples, method=\"linear\")\n",
+    "\n",
+    "kabs = kabs.reshape((nsamples, -1))*u.cm**2/u.g\n",
+    "ksca = ksca.reshape((nsamples, -1))*u.cm**2/u.g\n",
+    "\n",
+    "\"\"\"kabs = 10.**interp1d(np.log10(data[\"lam\"]), np.log10(data[\"kabs\"]), kind=\"linear\", axis=-1)(np.log10(lam.value))*u.cm**2/u.g\n",
+    "ksca = 10.**interp1d(np.log10(data[\"lam\"]), np.log10(data[\"ksca\"]), kind=\"linear\", axis=-1)(np.log10(lam.value))*u.cm**2/u.g\n",
+    "\n",
+    "p, amax = np.meshgrid(data[\"p\"], data[\"amax\"], indexing=\"ij\")\n",
+    "amax *= u.cm\"\"\"\n",
+    "\n",
+    "# Create the Dust object.\n",
+    "d = Dust(lam=lam, \n",
+    "         amax=amax, \n",
+    "         p=p, \n",
+    "         kabs=kabs,\n",
+    "         ksca=ksca)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "adf95f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Trainer will use only 1 of 2 GPUs because it is running inside an interactive / notebook environment. You may try to set `Trainer(devices=2)` but please note that multi-GPU inside interactive / notebook environments is considered experimental and unstable. Your mileage may vary.\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "\n",
+      "  | Name  | Type                 | Params | Mode \n",
+      "-------------------------------------------------------\n",
+      "0 | model | MultiLayerPerceptron | 5.0 K  | train\n",
+      "-------------------------------------------------------\n",
+      "5.0 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "5.0 K     Total params\n",
+      "0.020     Total estimated model params size (MB)\n",
+      "10        Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
+    },
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+       "version_minor": 0
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+      "text/plain": [
+       "Sanity Checking: |                                                                                            …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "Training: |                                                                                                   …"
+      ]
+     },
+     "metadata": {},
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+    },
+    {
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+       "Validation: |                                                                                                 …"
+      ]
+     },
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+      "text/plain": [
+       "Validation: |                                                                                                 …"
+      ]
+     },
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+      "text/plain": [
+       "Validation: |                                                                                                 …"
+      ]
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+       "Validation: |                                                                                                 …"
+      ]
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+       "Validation: |                                                                                                 …"
+      ]
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+       "Validation: |                                                                                                 …"
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+       "Validation: |                                                                                                 …"
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+       "Validation: |                                                                                                 …"
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+       "Validation: |                                                                                                 …"
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+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "       Test metric             DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "        test_loss         1.5256998267432209e-05\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
+     ]
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",
+      "text/plain": [
+       "<Figure size 1100x1100 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#for model in [\"kabs\", \"ksca\", \"pmo\", \"random_nu\"]:\n",
+    "for model in [\"random_nu\"]:\n",
+    "    if model in [\"kabs\", \"ksca\", \"pmo\"]:\n",
+    "        d.learn(model=model, hidden_units=(16,)*6, overwrite=True)\n",
+    "        batch_size = 1000\n",
+    "    else:\n",
+    "        d.learn(model=model, hidden_units=(48,)*6, overwrite=True)\n",
+    "        batch_size = 100000\n",
+    "\n",
+    "    d.fit(epochs=800, batch_size=batch_size, num_workers=50)\n",
+    "    d.test_model(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "711a4844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p: 4.150285326078643, amax: 0.2240181408039542, T: 296.5305719128035, abundances: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Good test: p: 3.4991353911167407, amax: 1.312355072991905, T: 2008.9396240632707\n",
+    "# Good test: p: 4.081916099538354, amax: 0.00019792861359660814, T: 1434.7155605917776\n",
+    "# Good test: p: 2.807472093033742, amax: 0.00011679887614606475, T: 408.08140708872327\n",
+    "d.plot_random_nu_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "63f9bd1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.save(\"diana_wice.dst\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc8c19b-b74c-4d89-bc27-581aa0e23946",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/dust-demo.ipynb b/examples/dust-demo.ipynb
index 993a4e7..23a7c35 100644
--- a/examples/dust-demo.ipynb
+++ b/examples/dust-demo.ipynb
@@ -2,11597 +2,100 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "461d2258",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pinballrt.dust import Dust, load\n",
-    "from astropy.modeling import models\n",
-    "import astropy.units as u\n",
-    "import numpy as np\n",
-    "import pinballrt\n",
-    "\n",
-    "from scipy.interpolate import interp1d\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "89f9421c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warp 1.8.0 initialized:\n",
-      "   CUDA not enabled in this build\n",
-      "   Devices:\n",
-      "     \"cpu\"      : \"i386\"\n",
-      "   Kernel cache:\n",
-      "     /Users/psheehan/Library/Caches/warp/1.8.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = np.load(os.path.join(os.path.dirname(pinballrt.dust.__file__), \"tests/data/diana_wice.npz\"))\n",
-    "\n",
-    "lam = np.logspace(np.log10(data[\"lam\"].min()), np.log10(data[\"lam\"].max()), 1000)*u.cm\n",
-    "kabs = 10.**interp1d(np.log10(data[\"lam\"]), np.log10(data[\"kabs\"]), kind=\"linear\", axis=-1)(np.log10(lam.value))*u.cm**2/u.g\n",
-    "ksca = 10.**interp1d(np.log10(data[\"lam\"]), np.log10(data[\"ksca\"]), kind=\"linear\", axis=-1)(np.log10(lam.value))*u.cm**2/u.g\n",
-    "\n",
-    "d = Dust(lam=lam, \n",
-    "         kabs=kabs, \n",
-    "         ksca=ksca, \n",
-    "         amax=data[\"amax\"]*u.cm, \n",
-    "         p=data[\"p\"], \n",
-    "         interpolate=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "adf95f57",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: False, used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /Users/psheehan/pinball-rt/examples/ksca_lightning_logs exists and is not empty.\n",
-      "\n",
-      "  | Name  | Type                 | Params | Mode \n",
-      "-------------------------------------------------------\n",
-      "0 | model | MultiLayerPerceptron | 1.4 K  | train\n",
-      "-------------------------------------------------------\n",
-      "1.4 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "1.4 K     Total params\n",
-      "0.006     Total estimated model params size (MB)\n",
-      "16        Modules in train mode\n",
-      "0         Modules in eval mode\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f039d5c6b4d42729513059eca35205e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a26b1890123945cb9fcf4d463ad27fdb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "546e78e36115435598852c0fc5b7010b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b3501e59b486434897fd52799e210e0f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "242767b0d0744cd681a27846bf63754c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c889f7a047834e0d990f8c44393b4402",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f064df582ab840268b785aac73947d65",
-       "version_major": 2,
-       "version_minor": 0
-      },
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for model in [\"kabs\", \"ksca\", \"pmo\"]:\n",
-    "    if model in [\"kabs\", \"ksca\"]:\n",
-    "        d.learn(model=model, nsamples=10000000, hidden_units=(16,)*6, overwrite=True)\n",
-    "    else:\n",
-    "        d.learn(model=model, nsamples=1000000, hidden_units=(16,)*6, overwrite=True)\n",
-    "\n",
-    "    d.fit(epochs=300, batch_size=10000)\n",
-    "    d.test_model(plot=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b4bb9e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "log10_amax: -1.6217233441241388, p: 4.262695318455325, log10_temperature: -1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d.plot_pmo_model()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "30125b57",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: False, used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n"
-     ]
-    }
-   ],
-   "source": [
-    "d.learn(model=\"random_nu\", hidden_units=3*(48,), nsamples=10000000, overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cb2e3007",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /Users/psheehan/pinball-rt/examples/random_nu_lightning_logs exists and is not empty.\n",
-      "\n",
-      "  | Name  | Type                 | Params | Mode \n",
-      "-------------------------------------------------------\n",
-      "0 | model | MultiLayerPerceptron | 5.0 K  | train\n",
-      "-------------------------------------------------------\n",
-      "5.0 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "5.0 K     Total params\n",
-      "0.020     Total estimated model params size (MB)\n",
-      "10        Modules in train mode\n",
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+   "execution_count": null,
+   "id": "461d2258",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "`Trainer.fit` stopped: `max_epochs=500` reached.\n"
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
    ],
    "source": [
-    "d.fit(epochs=500, batch_size=10000)"
+    "from pinballrt.dust import IsotropicDust, load, suggest_opacity_sampling\n",
+    "import astropy.units as u\n",
+    "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "daaf9c09",
+   "execution_count": null,
+   "id": "89f9421c",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:433: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
-     ]
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-      "text/plain": [
-       "Testing: |          | 0/? [00:00<?, ?it/s]"
-      ]
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     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "        test_loss          8.12177768239053e-06\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:433: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
+      "(4, 100, 1000)\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "692d75750cea48d6a27cf072cf7bcbfb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Predicting: |          | 0/? [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1100x1100 with 25 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "d.test_model(plot=True)"
+    "# Define the wavelength grid (in microns).\n",
+    "wavelengths = np.logspace(-1, 4, 1000) * u.micron\n",
+    "\n",
+    "samples = suggest_opacity_sampling(100, p_range=(2.5, 4.5), amax_range=(1*u.micron, 10*u.cm), n_dust_subspecies=3)\n",
+    "samples = np.moveaxis(np.repeat(np.expand_dims(samples, 1), wavelengths.size, axis=1), -1, 0)\n",
+    "\n",
+    "print(samples.shape)\n",
+    "\n",
+    "p = samples[0]\n",
+    "amax = samples[1] * u.cm\n",
+    "silicate_fraction = samples[2]\n",
+    "water_fraction = samples[3]\n",
+    "\n",
+    "wavelengths = np.repeat(np.expand_dims(wavelengths, axis=0), max(samples.shape[1], 1), axis=0)\n",
+    "\n",
+    "# Define the absorption and scattering opacities (in cm^2/g).\n",
+    "silicate_feature = 100**silicate_fraction * np.exp(-0.5*(wavelengths - 10*u.micron)**2 / (10.*u.micron)**2) * u.cm**2 / u.g\n",
+    "water_feature = 100**water_fraction * np.exp(-0.5*(wavelengths - 3.*u.micron)**2 / (2.*u.micron)**2) * u.cm**2 / u.g\n",
+    "\n",
+    "power_law_index = (-2. / (1 + np.exp(-p/3.5 * (-1.5 - np.log10(amax.to(u.cm).value)))))\n",
+    "\n",
+    "kappa_abs = 1.0 * (wavelengths.to(u.micron).value/100.0)**power_law_index * u.cm**2 / u.g + silicate_feature + water_feature\n",
+    "kappa_scat = 0.5 * (wavelengths.to(u.micron).value/100.0)**power_law_index * u.cm**2 / u.g + silicate_feature + water_feature\n",
+    "\n",
+    "# Create the Dust object.\n",
+    "d = IsotropicDust(lam=wavelengths[0,:], \n",
+    "                  amax=amax[:,0], \n",
+    "                  p=p[:,0], \n",
+    "                  abundances=(silicate_fraction[:,0], water_fraction[:,0]),\n",
+    "                  kabs=kappa_abs, \n",
+    "                  ksca=kappa_scat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "9da6a7d8",
+   "execution_count": null,
+   "id": "adf95f57",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "p: 4.27963641391967, amax: 0.0005084393300744386, T: 0.14284917153748797\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "d.plot_random_nu_model(100000)"
+    "for model in [\"kabs\", \"ksca\", \"pmo\", \"random_nu\"]:\n",
+    "    if model in [\"kabs\", \"ksca\", \"pmo\"]:\n",
+    "        d.learn(model=model, nsamples=wavelengths.size, hidden_units=(16,)*6, overwrite=True)\n",
+    "        batch_size = 10\n",
+    "    else:\n",
+    "        d.learn(model=model, nsamples=wavelengths.size, hidden_units=(48,)*3, overwrite=True)\n",
+    "        batch_size = 1000\n",
+    "\n",
+    "    d.fit(epochs=50, batch_size=batch_size)\n",
+    "    d.test_model(plot=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1527a8bf",
+   "id": "711a4844",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Bad:\n",
-    "#p: 4.480264827710928, amax: 0.00010237462737553694, T: 1687.739293126636 # Missing narrow peak at 10 microns, and too much power at long wavelengths"
+    "d.plot_random_nu_model()"
    ]
   },
   {
@@ -11622,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "1ee5dd93",
    "metadata": {},
    "outputs": [],
@@ -11632,22 +135,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "9c70d62b",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'd' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43md\u001b[49m.learn(model=\u001b[33m'\u001b[39m\u001b[33mml_step\u001b[39m\u001b[33m'\u001b[39m, nsamples=\u001b[32m16000000\u001b[39m, hidden_units=((\u001b[32m128\u001b[39m, \u001b[32m128\u001b[39m),)*\u001b[32m6\u001b[39m)\n",
-      "\u001b[31mNameError\u001b[39m: name 'd' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "d.learn(model='ml_step', nsamples=16000000, hidden_units=((128, 128),)*6)"
    ]
@@ -11690,13 +181,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d.save(\"amax.dst\")"
+    "d.save(\"dust.dst\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pinball",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -11710,7 +201,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/pinballrt/camera.py b/pinballrt/camera.py
index 37f16c6..b156f43 100644
--- a/pinballrt/camera.py
+++ b/pinballrt/camera.py
@@ -27,6 +27,9 @@ def set_orientation(self, incl, pa, dpc):
         self.i = np.array([self.r*np.sin(self.incl)*np.cos(phi), \
                 self.r*np.sin(self.incl)*np.sin(phi), \
                 self.r*np.cos(self.incl)])
+        
+        with wp.ScopedDevice(self.grid.device):
+            self.i_wp = wp.vec3(self.i)
 
         self.ex = np.array([-np.sin(phi), np.cos(phi), 0.0])
         self.ey = np.array([-np.cos(self.incl)*np.cos(phi), \
@@ -65,6 +68,9 @@ def emit_rays(self, x, y, nu, nx, ny, pixel_size):
         ray_list.temperature = wp.zeros(xflat.size, dtype=float)
         ray_list.amax = wp.zeros(xflat.size, dtype=float)
         ray_list.p = wp.zeros(xflat.size, dtype=float)
+        if self.grid.n_dust_abundances > 0:
+            ray_list.dust_abundances = wp.array2d(np.zeros((xflat.size, self.grid.n_dust_abundances)), dtype=float)
+        ray_list.opacities_out_of_date = wp.zeros(xflat.size, dtype=bool)
 
         ray_list.radius = wp.array(np.zeros(xflat.shape), dtype=float)
         if isinstance(self.grid, LogUniformSphericalGrid):
diff --git a/pinballrt/dust.py b/pinballrt/dust.py
index d46ec30..a9c598b 100644
--- a/pinballrt/dust.py
+++ b/pinballrt/dust.py
@@ -5,7 +5,6 @@
 from torch.utils.data import DataLoader, TensorDataset, random_split
 from scipy.spatial.transform import Rotation
 import pandas as pd
-import scipy.interpolate
 from astropy.modeling import models
 import astropy.units as u
 import astropy.constants as const
@@ -19,27 +18,34 @@
 import os
 import zuko
 
+from .utils import GridStruct, random_direction
+from .photons import PhotonList
+
 from torch.distributions.multivariate_normal import MultivariateNormal
 
 wp.config.quiet = True
 
 class Dust(pl.LightningDataModule):
-    def __init__(self, lam=None, kabs=None, ksca=None, amax=None, p=None, device="cpu"):
+    def __init__(self, lam=None, kabs=None, ksca=None, amax=None, p=None, abundances=(), device="cpu", ntemperatures=300):
         """
         Initialize the Dust module with wavelength, absorption, and scattering coefficients.
 
         Parameters
         ----------
         lam : astropy.units.Quantity
-            Wavelengths at which the dust opacities are defined.
+            Wavelengths at which the dust opacities are defined. Should be in units that convert to cm, and with a shape of (nwavelengths,).
         kabs : astropy.units.Quantity
-            Absorption coefficients of the dust.
+            Absorption coefficients of the dust. Should be in units that convert to cm^2/g, and with a shape of (nsamples, nwavelengths),
+            where ndims is the number of dimensions in the parameter space (e.g., p, amax, abundances).
         ksca : astropy.units.Quantity
-            Scattering coefficients of the dust.
+            Scattering coefficients of the dust. Should be in units that convert to cm^2/g, and with a shape of (nsamples, nwavelengths),
+            where ndims is the number of dimensions in the parameter space (e.g., p, amax, abundances).
         amax : astropy.units.Quantity
-            Maximum grain size for the size distribution.
+            Maximum grain size for the size distribution. Should be in units that convert to cm, and with a shape of (nsamples,).
         p : float
-            Power-law index for the grain size distribution.
+            Power-law index for the grain size distribution. Should have a shape of (nsamples,)
+        abundances : tuple of np.arrays
+            Tuple of arrays containing the abundances of different dust species. Each array should have a shape of (nsamples,).
         device : str
             Device to run the computations on (e.g., "cpu" or "cuda").
         """
@@ -47,24 +53,19 @@ def __init__(self, lam=None, kabs=None, ksca=None, amax=None, p=None, device="cp
 
         kunit = kabs.unit
         lam_unit = lam.unit
-        amax_unit = amax.unit
 
         lam = lam.value
-        amax = amax.value
-        p = p
         kabs = kabs.value
         ksca = ksca.value
 
         if lam[1] > lam[0]:
-            lam = lam[::-1]
+            lam = np.flip(lam, axis=-1)
             kabs = np.flip(kabs, axis=-1)
             ksca = np.flip(ksca, axis=-1)
 
         self.nu = (const.c / (lam * lam_unit)).decompose().to(u.GHz)
         self.kmean = np.mean(kabs) * kunit
         self.lam = lam * lam_unit
-        self.amax = amax * amax_unit
-        self.p = p
         self.kabs = kabs / self.kmean.value
         self.ksca = ksca / self.kmean.value
         self.kext = (kabs + ksca) / self.kmean.value
@@ -73,23 +74,36 @@ def __init__(self, lam=None, kabs=None, ksca=None, amax=None, p=None, device="cp
         self.log10_nu_min = np.log10(self.nu.value.min())
         self.log10_nu_max = np.log10(self.nu.value.max())
 
-        with wp.ScopedDevice(device):
-            self.nu_wp = wp.array(self.nu.value, dtype=float)
-            self.amax_wp = wp.array(self.amax.value, dtype=float)
-            self.p_wp = wp.array(self.p, dtype=float)
-            self.kabs_wp = wp.array3d(self.kabs, dtype=float)
-            self.ksca_wp = wp.array3d(self.ksca, dtype=float)
-            self.kext_wp = wp.array3d(self.kext, dtype=float)
-            self.albedo_wp = wp.array3d(self.albedo, dtype=float)
+        self.amax = amax
+        if amax is not None:
+            self.log10_amax = np.log10(amax.to(u.cm).value)
+        self.p = p
+        self.abundances = abundances
+
+        self.dims = ()
+        self.samples = ()
+        for dim in ["p", "log10_amax", "abundances"]:
+            if hasattr(self, dim) and getattr(self, dim) is not None and getattr(self, dim) is not ():
+                if dim in ["abundances"]:
+                    self.samples += getattr(self, dim)
+                else:
+                    self.samples += (getattr(self, dim),)
+                self.dims += (dim,)
+
+        if len(self.dims) > 0:
+            self.samples = np.vstack(self.samples).T
+        else:
+            self.samples = np.zeros((1,0))
+        self.ndims = len(self.dims) + (len(self.abundances) - 1 if len(abundances) > 0 else 0)
 
-        self.temperature = np.logspace(-1.,4.,999)
+        self.temperature = np.logspace(-1.,4.,ntemperatures)
         self.log_temperature = np.log10(self.temperature)
 
     def __getstate__(self):
         state = self.__dict__.copy()
                 
         for entry in state:
-            if isinstance(getattr(self, entry), wp.types.array):
+            if wp.types.is_array(getattr(self, entry)):
                 state[entry] = getattr(self, entry).numpy()
             else:
                 state[entry] = getattr(self, entry)
@@ -104,13 +118,6 @@ def __setstate__(self, state):
         self.__dict__.update(state)
 
     def to_device(self, device):
-        with wp.ScopedDevice(device):
-            self.nu_wp = wp.array(self.nu.value, dtype=float)
-            self.kabs_wp = wp.array(self.kabs, dtype=float)
-            self.ksca_wp = wp.array(self.ksca, dtype=float)
-            self.kext_wp = wp.array(self.kext, dtype=float)
-            self.albedo_wp = wp.array(self.albedo, dtype=float)
-
         for model in ["random_nu", "ml_step", "kabs", "ksca"]:
             if hasattr(self, f"{model}_model"):
                 getattr(self, f"{model}_model").to(device)
@@ -119,17 +126,13 @@ def to_device(self, device):
             if hasattr(self, f"{model}_y_scaler"):
                 getattr(self, f"{model}_y_scaler").to(device)
 
-    def interpolate_kabs(self, p, amax, nu):
-        samples = np.vstack((p.flatten(), np.log10(amax).flatten(), np.log10(nu).flatten())).T
-
-        interpolated = scipy.interpolate.interpn((self.p, np.log10(self.amax.value), np.log10(self.nu.value)), np.log10(self.kabs), samples, method="cubic")
-
-        return 10.**interpolated.reshape(p.shape)
-
-    def ml_kabs(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None):
+    def ml_kabs(self, p=None, amax=None, nu=None, abundances=None, photon_list=None, iphotons=None):
         if photon_list is not None:
             p = wp.to_torch(photon_list.p)
             amax = wp.to_torch(photon_list.amax)
+            if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
+
             if nu is None:
                 nu = wp.to_torch(photon_list.frequency)
 
@@ -137,28 +140,40 @@ def ml_kabs(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None):
                     nu = nu[iphotons]
                     p = p[iphotons]
                     amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
             else:
                 if nu.size(0) != p.size(0):
                     p = p[iphotons]
                     amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
 
-        samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(nu))), 0, 1)
+            abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
 
-        kabs = 10.**self.kabs_y_scaler.inverse_transform(self.kabs_model(self.kabs_x_scaler.transform(samples))).detach().flatten()
+        if amax is not None:
+            log10_amax = torch.log10(amax)
 
-        return kabs
-    
-    def interpolate_ksca(self, p, amax, nu):
-        samples = np.vstack((p.flatten(), np.log10(amax).flatten(), np.log10(nu).flatten())).T
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(nu),)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
 
-        interpolated = scipy.interpolate.interpn((self.p, np.log10(self.amax.value), np.log10(self.nu.value)), np.log10(self.ksca), samples, method="cubic")
+        kabs = 10.**self.kabs_y_scaler.inverse_transform(self.kabs_model(self.kabs_x_scaler.transform(samples))).detach().flatten()
 
-        return 10.**interpolated.reshape(p.shape)
+        return kabs
 
-    def ml_ksca(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None):
+    def ml_ksca(self, p=None, amax=None, nu=None, abundances=None, photon_list=None, iphotons=None):
         if photon_list is not None:
             p = wp.to_torch(photon_list.p)
             amax = wp.to_torch(photon_list.amax)
+            if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
+                
             if nu is None:
                 nu = wp.to_torch(photon_list.frequency)
 
@@ -166,63 +181,65 @@ def ml_ksca(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None):
                     nu = nu[iphotons]
                     p = p[iphotons]
                     amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
             else:
                 if nu.size(0) != p.size(0):
                     p = p[iphotons]
                     amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
+
+            abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
 
-        samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(nu))), 0, 1)
+        if amax is not None:
+            log10_amax = torch.log10(amax)
+
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(nu),)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
 
         ksca = 10.**self.ksca_y_scaler.inverse_transform(self.ksca_model(self.ksca_x_scaler.transform(samples))).detach().flatten()
 
         return ksca
 
-    def interpolate_kext(self, p, amax, nu):
-        if nu.unit.is_equivalent(u.GHz):
-            nu = nu.to(u.GHz).value
-        elif nu.unit.is_equivalent(u.cm):
-            nu = (const.c / nu).decompose().to(u.GHz)
-        return self.interpolate_kabs(p, amax, nu.value) + self.interpolate_ksca(p, amax, nu.value)
-
-    def ml_kext(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None):
+    def ml_kext(self, p=None, amax=None, nu=None, abundances=None, photon_list=None, iphotons=None):
         if photon_list is not None:
             p = wp.to_torch(photon_list.p)
             amax = wp.to_torch(photon_list.amax)
+            if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
+
             if nu is None:
                 nu = wp.to_torch(photon_list.frequency)
             else:
                 if nu.size(0) != p.size(0):
                     p = p[iphotons]
                     amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
 
-        samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(nu))), 0, 1)
-
-        return 10.**self.kabs_y_scaler.inverse_transform(self.kabs_model(self.kabs_x_scaler.transform(samples))).detach().flatten() + \
-                10.**self.ksca_y_scaler.inverse_transform(self.ksca_model(self.ksca_x_scaler.transform(samples))).detach().flatten()
-
-    def interpolate_albedo(self, p, amax, nu):
-        kabs = self.interpolate_kabs(p, amax, nu)
-        ksca = self.interpolate_ksca(p, amax, nu)
+            abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
 
-        return ksca / (kabs + ksca)
+        if amax is not None:
+            log10_amax = torch.log10(amax)
 
-    def ml_albedo(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None):
-        if photon_list is not None:
-            p = wp.to_torch(photon_list.p)
-            amax = wp.to_torch(photon_list.amax)
-            if nu is None:
-                nu = wp.to_torch(photon_list.frequency)
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
             else:
-                if nu.size(0) != p.size(0):
-                    p = p[iphotons]
-                    amax = amax[iphotons]
-
-        samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(nu))), 0, 1)
-
-        kabs = 10.**self.kabs_y_scaler.inverse_transform(self.kabs_model(self.kabs_x_scaler.transform(samples))).detach().flatten()
-        ksca = 10.**self.ksca_y_scaler.inverse_transform(self.ksca_model(self.ksca_x_scaler.transform(samples))).detach().flatten()
+                samples += (eval(dim),)
+        samples += (torch.log10(nu),)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
 
-        return ksca / (kabs + ksca)
+        return 10.**self.kabs_y_scaler.inverse_transform(self.kabs_model(self.kabs_x_scaler.transform(samples))).detach().flatten() + \
+                10.**self.ksca_y_scaler.inverse_transform(self.ksca_model(self.ksca_x_scaler.transform(samples))).detach().flatten()
 
     def absorb(self, temperature):
         nphotons = frequency.numpy().size
@@ -236,61 +253,89 @@ def absorb(self, temperature):
         frequency = self.random_nu(temperature)
 
         return direction, frequency
+    
+    def update_photon_opacities(self, photon_list, iphotons, grid=None, inu=None):
+        nphotons = iphotons.size(0)
 
-    def random_nu_manual(self, p, amax, temperature, ksi=None, batch_size=100000):
-        import interpn
-
-        if ksi is None:
-            nphotons = temperature.size
-            ksi = np.random.rand(nphotons)
-
-        if not hasattr(self, "random_nu_CPD"):
-            random_nu_PDF = np.array([self.kabs * models.BlackBody(temperature=T*u.K)(self.nu) for T in self.temperature])
-            self.random_nu_CPD = scipy.integrate.cumulative_trapezoid(random_nu_PDF, self.nu, axis=-1, initial=0.)
-            self.random_nu_CPD /= self.random_nu_CPD[:,:,:,-1:]
-            self.drandom_nu_CPD_dT = np.gradient(self.random_nu_CPD, self.temperature, axis=0)
-
-        count = 0
-
-        dims = (self.temperature.size, self.p.size, self.amax.size, self.nu.size)
-        starts = np.array([np.log10(self.temperature.min()), self.p.min(), np.log10(self.amax.min().value), np.log10(self.nu.min().value)])
-        steps = np.array([(np.log10(self.temperature[1]) - np.log10(self.temperature[0])), self.p[1] - self.p[0], (np.log10(self.amax[1].value) - np.log10(self.amax[0].value)), (np.log10(self.nu[1].value) - np.log10(self.nu[0].value))])
-
-        interpolator = interpn.MulticubicRegular.new(dims, starts, steps, self.random_nu_CPD)
-
-        frequency = []
-        while count < temperature.size:
-            n = min(batch_size, temperature.size - count)
-            p_batch = p[count:count+n]
-            amax_batch = amax[count:count+n]
-            temperature_batch = temperature[count:count+n]
-            ksi_batch = ksi[count:count+n]
-
-            samples = np.vstack([np.repeat(np.array([np.log10(temperature_batch), p_batch, np.log10(amax_batch.value)]), self.nu.size, axis=1), 
-                                 np.tile(np.log10(self.nu.value), p_batch.size)])
+        if grid is not None and inu is not None:
+            wp.launch(kernel=self.set_photon_opacities_grid,
+                      dim=(nphotons,),
+                      inputs=[photon_list, grid, inu, iphotons])
+        else:
+            wp.launch(kernel=self.set_photon_opacities,
+                      dim=(nphotons,),
+                      inputs=[photon_list, 
+                              self.ml_kabs(photon_list=photon_list, iphotons=iphotons), 
+                              self.ml_ksca(photon_list=photon_list, iphotons=iphotons), 
+                              iphotons])
+
+    @wp.kernel
+    def set_photon_opacities(photon_list: PhotonList,
+                          kabs: wp.array(dtype=float),
+                          ksca: wp.array(dtype=float),
+                          iphotons: wp.array(dtype=int)): # pragma: no cover
+        i = wp.tid()
+        ip = iphotons[i]
+
+        photon_list.kabs[ip] = kabs[i]
+        photon_list.ksca[ip] = ksca[i]
+        photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i])
+
+    @wp.kernel
+    def set_photon_opacities_grid(photon_list: PhotonList,
+                                  grid: GridStruct,
+                                  inu: int,
+                                  iphotons: wp.array(dtype=int)): # pragma: no cover
+        ip = iphotons[wp.tid()]
+
+        ix, iy, iz = photon_list.indices[ip][0], photon_list.indices[ip][1], photon_list.indices[ip][2]
+
+        photon_list.kabs[ip] = grid.kabs[inu, ix, iy, iz]
+        photon_list.ksca[ip] = grid.ksca[inu, ix, iy, iz]
+        photon_list.albedo[ip] = photon_list.ksca[ip] / (photon_list.kabs[ip] + photon_list.ksca[ip])
+
+    def set_grid_opacities(self, grid, frequency):
+        p = wp.to_torch(grid.p)
+        shape = p.shape
+        p = p.flatten()
+        amax = wp.to_torch(grid.amax).flatten()
+        abundances = tuple([wp.to_torch(grid.dust_abundances)[i].flatten() for i in range(len(self.abundances))])
+
+        kabs = [self.ml_kabs(p=p, 
+                             amax=amax, 
+                             abundances=abundances, 
+                             nu=torch.ones(np.prod(shape), dtype=torch.float32, 
+                                           device=wp.device_to_torch(wp.get_device())) * \
+                                            f.to(u.GHz).value) for f in frequency]
+
+        grid.kabs = wp.from_torch(torch.concatenate(kabs).reshape((len(frequency),) + shape))
+
+        ksca = [self.ml_ksca(p=p, 
+                             amax=amax, 
+                             abundances=abundances, 
+                             nu=torch.ones(np.prod(shape), dtype=torch.float32, 
+                                           device=wp.device_to_torch(wp.get_device())) * 
+                                            f.to(u.GHz).value) for f in frequency]
+        
+        grid.ksca = wp.from_torch(torch.concatenate(ksca).reshape((len(frequency),) + shape))
 
-            random_nu_CPD = interpolator.eval(samples)
-            random_nu_CPD = random_nu_CPD.reshape((p_batch.size, self.nu.size))
+    def random_nu_ml(self, p, amax, temperature, abundances=None):
+        nphotons = temperature.size
+        ksi = torch.rand(int(nphotons), dtype=torch.float32)
+        ksi = torch.clamp(torch.arctanh(2*ksi - 1.), min=-8.6643, max=8.6643)
 
-            i = np.argmax(ksi_batch[:,np.newaxis] < random_nu_CPD, axis=1)
-    
-            frequency_batch = (ksi_batch - random_nu_CPD[np.arange(random_nu_CPD.shape[0]),i-1]) * (self.nu[i] - self.nu[i-1]) / \
-                    (random_nu_CPD[np.arange(random_nu_CPD.shape[0]),i] - random_nu_CPD[np.arange(random_nu_CPD.shape[0]),i-1]) + \
-                    self.nu[i-1]
-            
-            frequency.append(frequency_batch)
-            
-            count += n
+        log10_amax = np.log10(amax)
 
-        frequency = np.concatenate(frequency).value
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += tuple([torch.tensor(a, dtype=torch.float32) for a in abundances])
+            else:
+                samples += (torch.tensor(eval(dim), dtype=torch.float32),)
+        samples += (torch.log10(torch.tensor(temperature, dtype=torch.float32)), ksi)
 
-        return frequency
-    
-    def random_nu_ml(self, p, amax, temperature):
-        nphotons = temperature.size
-        ksi = torch.rand(int(nphotons), device=wp.device_to_torch(wp.get_device()), dtype=torch.float32)
-        test_x = torch.transpose(torch.vstack((torch.tensor(p, dtype=torch.float32), torch.log10(torch.tensor(amax, dtype=torch.float32)), torch.log10(torch.tensor(temperature, dtype=torch.float32)), ksi)), 0, 1)
-        test_x = self.random_nu_x_scaler.transform(test_x)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
+        test_x = self.random_nu_x_scaler.transform(samples)
 
         log10_nu = torch.clamp(self.random_nu_y_scaler.inverse_transform(self.random_nu_model(test_x).detach()), self.log10_nu_min, self.log10_nu_max)
 
@@ -300,16 +345,35 @@ def random_nu(self, photon_list, subset=None):
         p = wp.to_torch(photon_list.p)
         amax = wp.to_torch(photon_list.amax)
         temperature = wp.to_torch(photon_list.temperature)
+        if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
         if subset is not None:
             p = p[subset]
             amax = amax[subset]
             temperature = temperature[subset]
+            if photon_list.dust_abundances is not None:
+                abundances = abundances[subset]
+
+        abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
             
         nphotons = temperature.size(0)
         ksi = torch.rand(int(nphotons), device=wp.device_to_torch(wp.get_device()), dtype=torch.float32)
+        ksi = torch.clamp(torch.arctanh(2*ksi - 1.), min=-8.6643, max=8.6643)
+
+        if amax is not None:
+            log10_amax = torch.log10(amax)
+
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(temperature), ksi)
 
-        test_x = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(temperature), ksi)), 0, 1)
-        test_x = self.random_nu_x_scaler.transform(test_x)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
+
+        test_x = self.random_nu_x_scaler.transform(samples)
 
         if nphotons > 250000:
             test_x = TensorDataset(test_x)
@@ -323,15 +387,18 @@ def random_nu(self, photon_list, subset=None):
 
         return nu
 
-    def planck_mean_opacity(self, p, amax, temperature):
-        vectorized_bb = np.vectorize(lambda p, a, T: self.kmean.cgs.value * scipy.integrate.trapezoid(self.ml_kabs(torch.tensor(p, dtype=torch.float32).expand(self.nu.size), 
-                torch.tensor(a, dtype=torch.float32).expand(self.nu.size), torch.tensor(self.nu.value, dtype=torch.float32)) * \
-                models.BlackBody(temperature=T*u.K)(self.nu).cgs.value, self.nu.to(u.Hz).value) * np.pi / (const.sigma_sb.cgs.value * T**4))
-
-        return vectorized_bb(p, amax, temperature)
+    def ml_planck_mean_opacity(self, p, amax, temperature, abundances=()):
+        log10_amax = torch.log10(amax)
+        
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(temperature),)
 
-    def ml_planck_mean_opacity(self, p, amax, temperature):
-        samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(temperature))), 0, 1)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
 
         return 10.**self.pmo_y_scaler.inverse_transform(self.pmo_model(self.pmo_x_scaler.transform(samples))).detach().flatten()
 
@@ -350,17 +417,11 @@ def ml_step(self, photon_list, s, iphotons):
 
         return 10.**torch.clamp(test_x[:,0], self.log10_nu0_min, self.log10_nu0_max), 10.**test_x[:,1], 10.**test_x[:,2], test_x[:,3], test_x[:,4], torch.zeros(test_x.size(0)), test_x[:,5], test_x[:,6], torch.zeros(test_x.size(0))
 
-    def initialize_model(self, model="random_nu", input_size=2, output_size=1, hidden_units=(48, 48, 48)):
-        if model == 'ml_step':
-            self.ml_step_model = NeuralSplineFlow(input_size, output_size, transforms=len(hidden_units), hidden_features=hidden_units[0])
-        elif model == 'random_nu':
-            self.random_nu_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units)
-        elif model == "kabs":
-            self.kabs_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units)
-        elif model == "ksca":
-            self.ksca_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units)
-        elif model == "pmo":
-            self.pmo_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units)
+    def initialize_model(self, model="random_nu", model_type="MLP", input_size=2, output_size=1, hidden_units=(48, 48, 48)):
+        if model_type == 'flow':
+            setattr(self, f"{model}_model", NeuralSplineFlow(input_size, output_size, transforms=len(hidden_units), hidden_features=hidden_units[0]))
+        else:
+            setattr(self, f"{model}_model", MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units))
 
     def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split=0.2, hidden_units=(48, 48, 48),
             tau_range=(3.0, 1e4), temperature_range=(0.1*u.K, 1e4*u.K), amax_range=(1*u.micron, 10.0*u.cm), p_range=(2.5, 4.5), 
@@ -398,12 +459,18 @@ def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split=
         self.learning = model
         self.overwrite = overwrite
 
+        # Reset the batch_size
+        if hasattr(self, "batch_size"):
+            del self.batch_size
+
         # Set up the NN
 
         if model == "random_nu":
-            input_size, output_size = 4, 1
+            self.input_size, self.output_size = self.ndims + 2, 1
+            self.model_type = "MLP"
         elif model == "ml_step":
-            input_size, output_size = 7, 5
+            self.input_size, self.output_size = 7, self.ndims + 3
+            self.model_type = "flow"
 
             if nu_range is None:
                 nu_range = (self.nu.value.min(), self.nu.value.max())
@@ -418,10 +485,11 @@ def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split=
             self.p_max = p_range[1]
             self.log10_tau_cell_nu0_min = np.log10(tau_range[0])
             self.log10_tau_cell_nu0_max = np.log10(tau_range[1])
-        elif model in ["kabs", "ksca","pmo"]:
-            input_size, output_size = 3, 1
+        elif model in ["kabs", "ksca", "g", "pmo"]:
+            self.input_size, self.output_size = self.ndims + 1, 1
+            self.model_type = "MLP"
 
-        self.initialize_model(model=model, input_size=input_size, output_size=output_size, hidden_units=hidden_units)
+        self.initialize_model(model=model, model_type=self.model_type, input_size=self.input_size, output_size=self.output_size, hidden_units=hidden_units)
 
         # Wrap the model in lightning
 
@@ -465,10 +533,10 @@ def test_model(self, plot=False):
 
             if self.current_model == "random_nu":
                 self.plot_triangle_plots(model=self.current_model)
-            elif self.current_model in ["kabs", "ksca"]:
+            elif self.current_model in ["kabs", "ksca", "g", "pmo"]:
                 self.plot_opacity_model(model=self.current_model)
-            elif self.current_model == "pmo":
-                self.plot_pmo_model()
+            elif self.current_model == "scattering_phase_function":
+                self.plot_scattering_phase_function_model()
             else:
                 #self.plot_ml_step()
                 self.plot_triangle_plots(model=self.current_model)
@@ -478,21 +546,18 @@ def prepare_data(self):
             return
         
         if hasattr(self, f"prepare_data_{self.current_model}"):
-            getattr(self, f"prepare_data_{self.current_model}")()
+            if self.current_model in ["kabs", "ksca"]:
+                samples, targets = self.prepare_data_opacity(model=self.current_model)
+            else:
+                samples, targets = getattr(self, f"prepare_data_{self.current_model}")()
         else:
             raise NotImplementedError(f"Data preparation for model {self.current_model} not implemented.")
 
-    def prepare_data_random_nu(self):
-        sampler = scipy.stats.qmc.LatinHypercube(d=4)
-        samples = sampler.random(self.nsamples)
-
-        samples[:,0] = samples[:,0] * (self.p.max() - self.p.min()) + self.p.min()
-        samples[:,1] = samples[:,1] * (np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value)
-        samples[:,2] = samples[:,2] * (np.log10(self.temperature.max()) - np.log10(self.temperature.min())) + np.log10(self.temperature.min())
+        self.nsamples = samples.shape[0]
 
         X = torch.tensor(samples, dtype=torch.float32)
-        y = torch.tensor(np.log10(self.random_nu_manual(samples[:,0], 10.**samples[:,1]*self.amax.unit, 10.**samples[:,2], ksi=samples[:,3])), dtype=torch.float32)
-
+        y = torch.tensor(targets, dtype=torch.float32)
+        
         X_scaler = StandardScaler()
         X_scaler.fit(X)
         X = X_scaler.transform(X)
@@ -501,88 +566,90 @@ def prepare_data_random_nu(self):
         y_scaler.fit(y)
         y = y_scaler.transform(y)
 
-        self.random_nu_x_scaler = X_scaler
-        self.random_nu_y_scaler = y_scaler
+        setattr(self, f"{self.current_model}_x_scaler", X_scaler)
+        setattr(self, f"{self.current_model}_y_scaler", y_scaler)
 
         self.dataset = TensorDataset(X, y)
 
-    def prepare_data_kabs(self):
-        sampler = scipy.stats.qmc.LatinHypercube(d=3)
-        samples = sampler.random(self.nsamples)
-
-        samples[:,0] = samples[:,0] * (self.p.max() - self.p.min()) + self.p.min()
-        samples[:,1] = samples[:,1] * (np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value)
-        samples[:,2] = samples[:,2] * (np.log10(self.nu.max().value) - np.log10(self.nu.min().value)) + np.log10(self.nu.min().value)
-
-        log10_kabs = scipy.interpolate.interpn((self.p, np.log10(self.amax.value), np.log10(self.nu.value)), np.log10(self.kabs), samples, method="cubic")
-
-        X = torch.tensor(samples, dtype=torch.float32)
-        y = torch.tensor(log10_kabs, dtype=torch.float32)
-        
-        X_scaler = StandardScaler()
-        X_scaler.fit(X)
-        X = X_scaler.transform(X)
-
-        y_scaler = StandardScaler()
-        y_scaler.fit(y)
-        y = y_scaler.transform(y)
+    def prepare_data_random_nu(self):
+        count = 0
+        batch_size = 100
 
-        self.kabs_x_scaler = X_scaler
-        self.kabs_y_scaler = y_scaler
+        total_samples = []
+        total_targets = []
+        total_original_indices = []
 
-        self.dataset = TensorDataset(X, y)
+        while count < self.samples.shape[0]:
+            n = min(batch_size, self.samples.shape[0] - count)
+            
+            samples = np.repeat(np.expand_dims(np.repeat(np.expand_dims(self.samples[count:count+n,:], 1), self.temperature.size, axis=1), 1), self.nu.size, 1)
+            original_indices = np.tile(np.expand_dims(np.arange(self.samples.shape[0])[count:count+n], axis=(-1, -2)), (1, self.temperature.size, self.nu.size)).flatten()
+    
+            temperature = np.repeat(np.expand_dims(np.repeat(np.expand_dims(self.temperature*u.K, (0, -1)), self.nu.size, axis=0), 0), max(samples.shape[0], 1), axis=0)
+            nu = np.repeat(np.repeat(np.expand_dims(self.nu, (0, -1, -2)), self.temperature.size, axis=2), max(samples.shape[0], 1), axis=0)
+            
+            ksi = scipy.integrate.cumulative_trapezoid(np.repeat(np.expand_dims(self.kabs[count:count+n,:], (-1, -2)), self.temperature.size, axis=-2) * models.BlackBody(temperature)(nu), np.log10(self.nu.to(u.GHz).value), axis=1, initial=0)
+            ksi /= ksi[:,-1:,:,:]
+            
+            samples = np.concat((samples, np.log10(temperature.to(u.K).value), ksi), axis=-1)
+            samples = samples.reshape((-1, samples.shape[-1]))
+            
+            targets = np.log10(nu.to(u.GHz).value.flatten())
+    
+            samples = samples.astype(np.float32)
+    
+            good = np.logical_not(np.logical_or(samples[:,-1] < 2e-8, samples[:,-1] == 1))
+    
+            samples, targets = samples[good,:], targets[good]
+            original_indices = original_indices[good]
+    
+            samples[:,-1] = 2*samples[:,-1] - 1
+            samples[:,-1] = np.arctanh(samples[:,-1])
 
-    def prepare_data_ksca(self):
-        sampler = scipy.stats.qmc.LatinHypercube(d=3)
-        samples = sampler.random(self.nsamples)
+            total_samples.append(samples)
+            total_targets.append(targets)
+            total_original_indices.append(original_indices)
 
-        samples[:,0] = samples[:,0] * (self.p.max() - self.p.min()) + self.p.min()
-        samples[:,1] = samples[:,1] * (np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value)
-        samples[:,2] = samples[:,2] * (np.log10(self.nu.max().value) - np.log10(self.nu.min().value)) + np.log10(self.nu.min().value)
+            count += n
 
-        log10_ksca = scipy.interpolate.interpn((self.p, np.log10(self.amax.value), np.log10(self.nu.value)), np.log10(self.ksca), samples, method="cubic")
+        samples = np.concatenate(total_samples, axis=0)
+        targets = np.concatenate(total_targets, axis=0)
+        self.original_indices = np.concatenate(total_original_indices, axis=0)
 
-        X = torch.tensor(samples, dtype=torch.float32)
-        y = torch.tensor(log10_ksca, dtype=torch.float32)
+        return samples, targets
 
-        X_scaler = StandardScaler()
-        X_scaler.fit(X)
-        X = X_scaler.transform(X)
+    def prepare_data_opacity(self, model="kabs"):  
+        samples = np.moveaxis(np.repeat(np.expand_dims(self.samples, 1), self.nu.size, axis=1), -1, 0)
+        self.original_indices = np.repeat(np.expand_dims(np.arange(self.samples.shape[0]), axis=-1), self.nu.size, axis=-1).flatten()
 
-        y_scaler = StandardScaler()
-        y_scaler.fit(y)
-        y = y_scaler.transform(y)
+        samples = np.concat((samples, np.expand_dims(np.repeat(np.expand_dims(np.log10(self.nu.to(u.GHz).value), axis=0), max(samples.shape[1], 1), axis=0), axis=0)), axis=0)
+        samples = samples.reshape((samples.shape[0], -1)).T
 
-        self.ksca_x_scaler = X_scaler
-        self.ksca_y_scaler = y_scaler
+        targets = np.log10(getattr(self, model).flatten())
 
-        self.dataset = TensorDataset(X, y)
+        return samples, targets
 
-    def prepare_data_pmo(self, device='cpu'):
-        sampler = scipy.stats.qmc.LatinHypercube(d=3)
-        samples = sampler.random(self.nsamples)
+    def prepare_data_kabs(self):
+        return self.prepare_data_opacity(model="kabs")
 
-        samples[:,0] = samples[:,0] * (self.p.max() - self.p.min()) + self.p.min()
-        samples[:,1] = samples[:,1] * (np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value)
-        samples[:,2] = samples[:,2] * (np.log10(self.temperature.max()) - np.log10(self.temperature.min())) + np.log10(self.temperature.min())
+    def prepare_data_ksca(self):
+        return self.prepare_data_opacity(model="ksca")
 
-        log10_pmo = np.log10(self.planck_mean_opacity(samples[:,0], 10.**samples[:,1], 10.**samples[:,2]))
+    def prepare_data_pmo(self):
+        temperature = np.repeat(np.expand_dims(self.temperature, (0, -1)), self.nu.size, axis=-1)
+        nu = np.repeat(np.expand_dims(self.nu, (0, 1)), self.temperature.size, axis=1)
 
-        X = torch.tensor(samples, dtype=torch.float32)
-        y = torch.tensor(log10_pmo, dtype=torch.float32)
+        self.pmo = self.kmean.cgs.value * scipy.integrate.trapezoid(models.BlackBody(temperature*u.K)(nu).cgs.value * np.expand_dims(self.kabs, 1), self.nu.to(u.Hz).value, axis=-1) * np.pi / (const.sigma_sb.cgs.value * self.temperature**4)
 
-        X_scaler = StandardScaler()
-        X_scaler.fit(X)
-        X = X_scaler.transform(X)
+        samples = np.moveaxis(np.repeat(np.expand_dims(self.samples, 1), self.temperature.size, axis=1), -1, 0)
+        self.original_indices = np.repeat(np.expand_dims(np.arange(self.samples.shape[0]), axis=-1), self.temperature.size, axis=-1).flatten()
 
-        y_scaler = StandardScaler()
-        y_scaler.fit(y)
-        y = y_scaler.transform(y)
+        samples = np.concat((samples, np.expand_dims(np.repeat(np.expand_dims(np.log10(self.temperature), axis=0), max(samples.shape[1], 1), axis=0), axis=0)), axis=0)
+        samples = samples.reshape((samples.shape[0], -1)).T
 
-        self.pmo_x_scaler = X_scaler
-        self.pmo_y_scaler = y_scaler
+        targets = np.log10(self.pmo).flatten()
 
-        self.dataset = TensorDataset(X, y)
+        return samples, targets
 
     def prepare_data_ml_step(self, device='cpu'):
         if os.path.exists("sim_results.csv"):
@@ -608,15 +675,19 @@ def prepare_data_ml_step(self, device='cpu'):
             df.to_csv("sim_results.csv")
 
         features = ["log10_nu", "log10_Eabs", "log10_tau", "yaw", "pitch", "direction_yaw", "direction_pitch"]
-        targets = ["log10_nu0", "log10_T", "log10_amax", "p", "log10_tau_cell_nu0"]
+        targets = ["log10_nu0", "log10_T"] + \
+                  (["log10_amax"] if "log10_amax" in self.dims else []) + \
+                  (["p"] if "p" in self.dims else []) + \
+                  ([f"abundance{i}" for i in range(len(self.abundances))]) + \
+                  ["log10_tau_cell_nu0"]
 
         df.loc[:, "log10_tau"] = np.where(np.logical_or(df["log10_tau"] < -6.5, np.isnan(df["log10_tau"].values)), np.log10(-np.log(1. - np.random.rand(len(df)))), df["log10_tau"])
 
         data = df.loc[:, targets+features].values
-        self.ml_step_x_scaler = StandardScaler()
-        self.ml_step_x_scaler.fit(torch.tensor(df.loc[:, features].values, dtype=torch.float32))
-        self.ml_step_y_scaler = StandardScaler()
-        self.ml_step_y_scaler.fit(torch.tensor(df.loc[:, targets].values, dtype=torch.float32))
+
+        samples = df.loc[:, features].values
+        targets = df.loc[:, targets].values
+        
         self.ml_step_features = features
         self.ml_step_limits = {}
         for key in features:
@@ -625,14 +696,11 @@ def prepare_data_ml_step(self, device='cpu'):
         self.df = df
         self.nsamples = len(df)
 
-        X = self.ml_step_x_scaler.transform(torch.tensor(df.loc[:, features].values, dtype=torch.float32))
-        y = self.ml_step_y_scaler.transform(torch.tensor(df.loc[:, targets].values, dtype=torch.float32))
-
-        self.dataset = TensorDataset(X, y)
+        return samples, targets
 
     def run_dust_simulation(self, nphotons=1000, tau_range=(3.0, 1e4), temperature_range=(0.1*u.K, 1e4*u.K), 
                             amax_range=(1*u.micron, 10.0*u.cm), p_range=(2.5, 4.5), nu_range=None, use_ml_step=False, 
-                            position=0):
+                            position=0, time_limit=np.inf, device="cpu"):
         """
         Run a dust simulation that can be used to learn an ML-step model with the given parameters.
 
@@ -660,7 +728,7 @@ def run_dust_simulation(self, nphotons=1000, tau_range=(3.0, 1e4), temperature_r
 
         # Set up the grid.
 
-        grid = UniformSphericalGrid(ncells=1, dr=1.0*u.au, mirror=False)
+        grid = UniformSphericalGrid(ncells=1, dr=1.0*u.au, mirror=False, device=device)
 
         density = np.ones(grid.shape) * 1e-16 * u.g / u.cm**3
 
@@ -671,22 +739,28 @@ def run_dust_simulation(self, nphotons=1000, tau_range=(3.0, 1e4), temperature_r
 
         photon_list = grid.emit(nphotons, wavelength="random", scattering=False)
 
-        initial_direction = np.zeros((nphotons, 3), dtype=np.float32)
-        initial_direction[:,0] = 1.
-        photon_list.direction = wp.array(initial_direction, dtype=wp.vec3)
-
-        photon_list.frequency = wp.array(10.**np.random.uniform(np.log10(nu_range[0].value), np.log10(nu_range[1].value), nphotons), dtype=float)
-        original_frequency = photon_list.frequency.numpy().copy()
-
-        photon_list.temperature = wp.array(10.**np.random.uniform(np.log10(temperature_range[0].to(u.K).value), np.log10(temperature_range[1].to(u.K).value), nphotons), dtype=float)
-
-        photon_list.amax = wp.array(10.**np.random.uniform(np.log10(amax_range[0].to(u.cm).value), np.log10(amax_range[1].to(u.cm).value), nphotons), dtype=float)
-        photon_list.p = wp.array(np.random.uniform(p_range[0], p_range[1], nphotons), dtype=float)
-
-        tau = 10.**np.random.uniform(np.log10(tau_range[0]), np.log10(tau_range[1]), nphotons)
-        photon_list.density = wp.array((tau / (self.kmean * self.interpolate_kabs(photon_list.p.numpy(), photon_list.amax.numpy(), photon_list.frequency.numpy()) * 1.*u.au) * self.kmean).to(1 / u.au), dtype=float)
+        with wp.ScopedDevice(grid.device):
+            initial_direction = np.zeros((nphotons, 3), dtype=np.float32)
+            initial_direction[:,0] = 1.
+            photon_list.direction = wp.array(initial_direction, dtype=wp.vec3)
+    
+            photon_list.frequency = wp.array(10.**np.random.uniform(np.log10(nu_range[0].value), np.log10(nu_range[1].value), nphotons), dtype=float)
+            original_frequency = photon_list.frequency.numpy().copy()
+    
+            photon_list.temperature = wp.array(10.**np.random.uniform(np.log10(temperature_range[0].to(u.K).value), np.log10(temperature_range[1].to(u.K).value), nphotons), dtype=float)
+    
+            samples = suggest_opacity_sampling(nphotons, p_range=p_range, amax_range=amax_range, n_dust_subspecies=len(self.abundances)+1, mode="random")
+    
+            photon_list.amax = wp.array(samples[:,1], dtype=float)
+            photon_list.p = wp.array(samples[:,0], dtype=float)
+            if len(self.abundances) > 0:
+                photon_list.dust_abundances = wp.array2d(samples[:,2:], dtype=float)
+    
+            tau = 10.**np.random.uniform(np.log10(tau_range[0]), np.log10(tau_range[1]), nphotons)
+            photon_list.density = wp.array((tau / (self.kmean * self.ml_kabs(photon_list=photon_list) * \
+                                                                                1.*u.au) * self.kmean).to(1 / u.au), dtype=float)
 
-        grid.propagate_photons(photon_list, learning=True, use_ml_step=use_ml_step)
+        grid.propagate_photons(photon_list, learning=True, use_ml_step=use_ml_step, time_limit=time_limit)
 
         # Calculate roll, pitch, and yaw for the position relative to where it started.
         # Also calculate roll, pitch, and yaw for the direction relative to the radial vector where it exits.
@@ -718,6 +792,9 @@ def run_dust_simulation(self, nphotons=1000, tau_range=(3.0, 1e4), temperature_r
                        "direction_yaw":direction_ypr[:,0],
                        "direction_pitch":direction_ypr[:,1]})
 
+        for i in range(len(self.abundances)):
+            df[f"abundance{i}"] = photon_list.dust_abundances.numpy()[:,i]
+
         return df
 
     # DataModule functions
@@ -772,7 +849,7 @@ def plot_ml_step(self, tau=30., temperature=100.0*u.K, amax=1.*u.micron, p=3.5,
 
         self.plot_triangle_plots(plot_columns=plot_columns)
 
-    def plot_opacity_model(self, model='kabs'):
+    def plot_opacity_model(self, model='kabs', show_scipy_interpolation=False):
         """
         Plot the learned opacity model against the interpolated opacity.
 
@@ -783,41 +860,70 @@ def plot_opacity_model(self, model='kabs'):
         """
         import matplotlib.pyplot as plt
 
-        log10_nu = np.linspace(np.log10(self.nu.min().value), np.log10(self.nu.max().value), 10)
-        log10_lam = np.log10((const.c / (10.**log10_nu * u.GHz)).to(u.cm).value)
-        log10_amax = np.repeat(np.random.uniform(0, 1, 1)*(np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value), 10)
-        p = np.repeat(np.random.uniform(0, 1, 1)*(self.p.max() - self.p.min()) + self.p.min(), 10)
-
-        print(f"log10_amax: {log10_amax[0]}, p: {p[0]}")
-
-        samples = np.vstack((p, log10_amax, log10_nu)).T
+        if hasattr(self, "test_indices") and len(self.test_indices) > 0:
+            index = np.random.choice(self.test_indices, size=1)[0]
+        else:
+            index = np.random.randint(0, self.samples.shape[0], 1)[0]
+
+        if model in ["kabs", "ksca", "g"]:
+            nx = self.nu.size
+        elif model in ["pmo"]:
+            nx = self.temperature.size
+
+        if "log10_amax" in self.dims:
+            log10_amax = np.repeat(np.log10(self.amax[index].to(u.cm).value), nx)
+        if "p" in self.dims:
+            p = np.repeat(self.p[index], nx)
+        if "abundances" in self.dims:
+            abundances = tuple([np.repeat(a[index], nx) for a in self.abundances])
+        
+        log10_lam = np.log10(self.lam.value)
+        log10_nu = np.log10(self.nu.to(u.GHz).value)
+        log10_temperature = np.log10(self.temperature)
 
-        interpolated = np.log10(getattr(self, f"interpolate_{model}")(p, 10.**log10_amax, 10.**log10_nu))
-        nned = getattr(self, f'{model}_y_scaler').inverse_transform(getattr(self, f'{model}_model')(getattr(self, f'{model}_x_scaler').transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy()
+        if model == "g":
+            interpolated = getattr(self, model)[index,:]
+        else:
+            interpolated = np.log10(getattr(self, model)[index,:])
 
-        plt.plot(log10_lam, interpolated)
-        plt.plot(log10_lam, nned)
-        plt.show()
+        print_str = ""
+        for dim in self.dims:
+            if dim == "abundances":
+                print_str += f"{dim}: {[abundances[i][0] for i in range(len(abundances))]}, "
+            else:
+                print_str += f"{dim}: {locals()[dim][0]}, "
+        print(print_str)
 
-    def plot_pmo_model(self):
-        """
-        Plot the learned Planck mean opacity model against the interpolated Planck mean opacity.
-        """
-        import matplotlib.pyplot as plt
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances":
+                samples += abundances
+            else:
+                samples += (locals()[dim],)
+        
+        if model in ["kabs", "ksca", "g"]:
+            samples = np.vstack(samples + (log10_nu,)).T
+            plot_x = log10_lam
+        else:
+            samples = np.vstack(samples + (log10_temperature,)).T
+            plot_x = log10_temperature
 
-        p = np.repeat(np.random.uniform(0, 1, 1)*(self.p.max() - self.p.min()) + self.p.min(), 100)
-        log10_amax = np.repeat(np.random.uniform(0, 1, 1)*(np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value), 100)
-        log10_temperature = np.linspace(np.log10(self.temperature.min()), np.log10(self.temperature.max()), 100)
+        nned = getattr(self, f'{model}_y_scaler').inverse_transform(getattr(self, f'{model}_model')(getattr(self, f'{model}_x_scaler').transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy()
 
-        print(f"log10_amax: {log10_amax[0]}, p: {p[0]}, log10_temperature: {log10_temperature[0]}")
+        plt.plot(plot_x, interpolated)
+        plt.plot(plot_x, nned)
 
-        samples = np.vstack((p, log10_amax, log10_temperature)).T
+        if show_scipy_interpolation:
+            X = getattr(self, f'{model}_x_scaler').inverse_transform(self.train.dataset.tensors[0][self.train.indices,:]).numpy()
+            y = getattr(self, f'{model}_y_scaler').inverse_transform(self.train.dataset.tensors[1][self.train.indices]).numpy()
 
-        interpolated = np.log10(self.planck_mean_opacity(p, 10.**log10_amax, 10.**log10_temperature))
-        nned = self.pmo_y_scaler.inverse_transform(self.pmo_model(self.pmo_x_scaler.transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy()
+            distance = np.sqrt((X[:,0] - self.p[index])**2 + (X[:,1] - np.log10(self.amax[index].to(u.cm).value))**2)
+            good = distance <= np.unique(np.sort(distance))[50]
+            
+            scipy_interpolated = scipy.interpolate.griddata(X[good,:], y[good], samples)
 
-        plt.plot(log10_temperature, interpolated)
-        plt.plot(log10_temperature, nned)
+            plt.plot(plot_x, scipy_interpolated)
+        
         plt.show()
 
     def plot_random_nu_model(self, nsamples=100000):
@@ -834,16 +940,18 @@ def plot_random_nu_model(self, nsamples=100000):
         T = np.repeat(10.**np.random.uniform(-1., 4., 1), nsamples)
         amax = np.repeat(10.**np.random.uniform(-4., 1., 1), nsamples)
         p = np.repeat(np.random.uniform(2.5, 4.5, 1), nsamples)
-        print(f"p: {p[0]}, amax: {amax[0]}, T: {T[0]}")
+        abundances = tuple([np.repeat(np.random.uniform(0, 1, 1), nsamples) for i in range(len(self.abundances))])
+        print(f"p: {p[0]}, amax: {amax[0]}, T: {T[0]}, abundances: {[abundances[i][0] for i in range(len(self.abundances))]}")
 
-        nu = self.random_nu_manual(p, amax*self.amax.unit, T)
-        nu2 = self.random_nu_ml(p, amax, T)
+        nu2 = self.random_nu_ml(p, amax, T, abundances=abundances)
 
-        counts, bins, _ = plt.hist(nu, 100)
-        plt.hist(nu2, 100)
+        counts, bins, _ = plt.hist(np.log10(nu2), 300)
 
         bb = models.BlackBody(temperature=T[0] * u.K)
-        pdf = bb(bins[1:]*u.GHz).value * self.interpolate_kabs(np.repeat(p[0], bins[1:].size), np.repeat(amax[0], bins[1:].size), bins[1:])
+        pdf = bb(10.**bins[1:]*u.GHz).value * self.ml_kabs(torch.tensor(p[0], dtype=torch.float32).repeat((bins[1:].size,)), 
+                                                           torch.tensor(amax[0], dtype=torch.float32).repeat((bins[1:].size,)), 
+                                                           torch.tensor(10.**bins[1:], dtype=torch.float32), 
+                                                           abundances=tuple([torch.tensor(a[0], dtype=torch.float32).repeat((bins[1:].size,)) for a in abundances])).numpy()
         pdf *= counts.max() / pdf.max()
 
         plt.plot(bins[1:], pdf, '-')
@@ -869,11 +977,11 @@ def plot_triangle_plots(self, model="ml_step", nsamples=200000, batch_size=100,
             self.num_workers = num_workers
 
         if self.trainer is not None:
-            if model == "ml_step":
+            if model in ["ml_step"]:
                 X_pred = self.trainer.predict(self.dustLM, datamodule=self)
                 X_pred = torch.cat(X_pred)
                 y_pred = torch.cat([batch[1] for batch in self.predict_dataloader()])
-            elif model == "random_nu":
+            elif model in ["random_nu", "random_direction"]:
                 y_pred = self.trainer.predict(self.dustLM, datamodule=self)
                 y_pred = torch.cat(y_pred)
                 X_pred = torch.cat([batch[0] for batch in self.predict_dataloader()])
@@ -885,11 +993,32 @@ def plot_triangle_plots(self, model="ml_step", nsamples=200000, batch_size=100,
 
         if model == "ml_step":
             features = np.array(["log10_nu", "log10_Eabs", "log10_tau", "yaw", "pitch", "direction_yaw", "direction_pitch"])
-            targets = np.array(["log10_nu0", "log10_T", "log10_amax", "p", "log10_tau_cell_nu0"])
+            targets = ["log10_nu0", "log10_T"] + \
+                       (["log10_amax"] if "log10_amax" in self.dims else []) + \
+                       (["p"] if "p" in self.dims else []) + \
+                       ([f"abundance{i}" for i in range(len(self.abundances))]) + \
+                       ["log10_tau_cell_nu0"]
         elif model == "random_nu":
-            features = np.array(["p", "log10_amax", "log10_temperature", "ksi"])
+            features = ()
+            for dim in self.dims:
+                if dim == "abundances":
+                    features += tuple([f"abundance_{i}" for i in range(len(self.abundances))])
+                else:
+                    features += (dim,)
+            features = np.array(features + ("log10_temperature", "ksi"))
             targets = np.array(["log10_nu"])
 
+            y_true = torch.unsqueeze(y_true, 1)
+        elif model == "random_direction":
+            features = ()
+            for dim in self.dims:
+                if dim == "abundances":
+                    features += tuple([f"abundance_{i}" for i in range(len(self.abundances))])
+                else:
+                    features += (dim,)
+            features = np.array(features + ("log10_nu", "ksi"))
+            targets = np.array(["theta"])
+
             y_true = torch.unsqueeze(y_true, 1)
 
         if isinstance(plot_columns, str) and plot_columns == 'all':
@@ -910,7 +1039,7 @@ def plot_triangle_plots(self, model="ml_step", nsamples=200000, batch_size=100,
                 if key1 == key2:
                     ax[i,j].hist(df_true[key1], bins=50, histtype='step', density=True)
                     if predict:
-                        ax[i,j].hist(df_pred[key1], bins=250, histtype='step', density=True)
+                        ax[i,j].hist(df_pred[key1], bins=50, histtype='step', density=True)
                 elif i > j:
                     ax[i,j].scatter(df_true[key2], df_true[key1], marker='.', s=1.0, alpha=1.0)
 
@@ -929,11 +1058,33 @@ def plot_triangle_plots(self, model="ml_step", nsamples=200000, batch_size=100,
     def setup(self, stage=None):
         if hasattr(self, "train") and hasattr(self, "valid") and hasattr(self, "test") and not self.overwrite:
             return
-        test_size = int(self.test_split * self.nsamples)
-        valid_size = int((self.nsamples - test_size)*self.valid_split)
-        train_size = self.nsamples - test_size - valid_size
-        train_val_tmp, self.test = random_split(self.dataset, [train_size + valid_size, test_size], generator=torch.Generator().manual_seed(1))
-        self.train, self.valid = random_split(train_val_tmp, [train_size, valid_size], generator=torch.Generator().manual_seed(2))
+        
+        if self.learning == "ml_step" or self.ndims == 0:
+            test_size = int(self.test_split * self.nsamples)
+            valid_size = int((self.nsamples - test_size)*self.valid_split)
+            train_size = self.nsamples - test_size - valid_size
+            train_val_tmp, self.test = random_split(self.dataset, [train_size + valid_size, test_size], generator=torch.Generator().manual_seed(1))
+            self.train, self.valid = random_split(train_val_tmp, [train_size, valid_size], generator=torch.Generator().manual_seed(2))
+        else:
+            train_indices, valid_indices, test_indices = torch.utils.data.random_split(range(self.samples.shape[0]), 
+                                                                                   (1.-(self.test_split + self.valid_split), 
+                                                                                    self.valid_split, self.test_split), 
+                                                                                   generator=torch.Generator().manual_seed(2))
+            self.test_indices = test_indices.indices
+
+            splits = np.repeat(0, self.dataset.tensors[0].size(0))
+            for ind in valid_indices.indices:
+                splits[self.original_indices == ind] = 1
+            for ind in test_indices.indices:
+                splits[self.original_indices == ind] = 2
+
+            train_indices = np.where(splits.flatten() == 0)[0]
+            valid_indices = np.where(splits.flatten() == 1)[0]
+            test_indices = np.where(splits.flatten() == 2)[0]
+
+            self.train = torch.utils.data.Subset(self.dataset, train_indices)
+            self.valid = torch.utils.data.Subset(self.dataset, valid_indices)
+            self.test = torch.utils.data.Subset(self.dataset, test_indices)
 
         if self.overwrite:
             self.overwrite = False
@@ -956,6 +1107,7 @@ def state_dict(self):
                 "lam": self.lam,
                 "amax": self.amax,
                 "p": self.p,
+                "abundances": self.abundances,
                 "kabs": self.kabs*self.kmean,
                 "ksca": self.ksca*self.kmean,
             },
@@ -999,6 +1151,574 @@ def save(self, filename):
     def copy(self, device="cpu"):
         return load(self.state_dict(), device=device)
 
+class IsotropicDust(Dust):
+    def scatter(self, photon_list, iphotons):
+        nphotons = iphotons.size(0)
+
+        wp.launch(kernel=random_direction,
+                  dim=(nphotons,),
+                  inputs=[photon_list.direction, iphotons, np.random.randint(0, 100000)])
+
+    def update_photon_scattering_phase_function(self, photon_list, direction, iphotons):
+        nphotons = iphotons.size(0)
+
+        wp.launch(kernel=self.scattering_phase_function_wp,
+                  dim=(nphotons,),
+                  inputs=[photon_list, direction, iphotons])
+
+    @wp.kernel
+    def scattering_phase_function_wp(photon_list: PhotonList,
+                                     direction: wp.vec3, 
+                                     iphotons: wp.array(dtype=int)): # pragma: no cover
+        i = wp.tid()
+        ip = iphotons[i]
+
+        photon_list.scattering_phase_function[ip] = 1.
+
+
+class HenyeyGreensteinDust(Dust):
+    def __init__(self, lam=None, kabs=None, ksca=None, g=None, amax=None, p=None, abundances=(), device="cpu", ntemperatures=1000):
+        """
+        Initialize the Henyey-Greenstein dust model.
+
+        Parameters
+        ----------
+        lam : numpy.ndarray
+            The wavelengths to use for the dust properties.
+        kabs : numpy.ndarray
+            The absorption opacities to use for the dust properties.
+        ksca : numpy.ndarray
+            The scattering opacities to use for the dust properties.
+        g : numpy.ndarray
+            The Henyey-Greenstein asymmetry parameter to use for the dust properties.
+        amax : float
+            The maximum grain size to use for the dust properties.
+        p : float
+            The power-law index for the grain size distribution.
+        device : str
+            The device to place the dust properties on ("cpu" or "cuda").
+        """
+        super().__init__(lam=lam, kabs=kabs, ksca=ksca, amax=amax, p=p, abundances=abundances, device=device, 
+                         ntemperatures=ntemperatures)
+
+        self.g = g
+
+    def to_device(self, device):
+        super().to_device(device)
+
+        for model in ["g"]:
+            if hasattr(self, f"{model}_model"):
+                getattr(self, f"{model}_model").to(device)
+            if hasattr(self, f"{model}_x_scaler"):
+                getattr(self, f"{model}_x_scaler").to(device)
+            if hasattr(self, f"{model}_y_scaler"):
+                getattr(self, f"{model}_y_scaler").to(device)
+
+    def scatter(self, photon_list, iphotons):
+        nphotons = iphotons.size(0)
+
+        wp.launch(kernel=self.random_direction,
+                  dim=(nphotons,),
+                  inputs=[photon_list.direction, photon_list.g, iphotons, np.random.randint(0, 100000)])
+
+    @wp.kernel
+    def random_direction(direction: wp.array(dtype=wp.vec3),
+                         g: wp.array(dtype=float),
+                         iphotons: wp.array(dtype=int),
+                         seed: int): # pragma: no cover
+        i = wp.tid()
+        ip = iphotons[i]
+
+        rng = wp.rand_init(seed, i)
+
+        cost = (1. + g[ip]**2. - ((1. - g[ip]**2.)/(1. - g[ip] + 2.*g[ip]*wp.randf(rng)))**2.) / (2. * g[ip])
+        theta = wp.acos(cost)
+        phi = 2.*np.pi*wp.randf(rng)
+
+        rpy_quat1 = wp.quat_rpy(phi, 0., 0.)
+        rpy_quat2 = wp.quat_rpy(0., theta, 0.)
+        direction_quat = wp.quat_between_vectors(wp.vec3(1., 0., 0.), direction[ip])
+        total_quat = direction_quat * rpy_quat1 * rpy_quat2
+
+        direction[ip] = wp.quat_rotate(total_quat, wp.vec3(1., 0., 0.))
+
+    def update_photon_scattering_phase_function(self, photon_list, direction, iphotons):
+        nphotons = iphotons.size(0)
+
+        wp.launch(kernel=self.scattering_phase_function_heneygreenstein_wp,
+                  dim=(nphotons,),
+                  inputs=[photon_list, direction, iphotons])
+
+    @wp.kernel
+    def scattering_phase_function_heneygreenstein_wp(photon_list: PhotonList,
+                                                     direction: wp.vec3, 
+                                                     iphotons: wp.array(dtype=int)): # pragma: no cover
+        i = wp.tid()
+        ip = iphotons[i]
+
+        mu = wp.dot(photon_list.direction[ip], direction)
+
+        photon_list.scattering_phase_function[ip] = (1. - photon_list.g[ip]**2.) / (1. + photon_list.g[ip]**2. - 2. * photon_list.g[ip] * mu)
+
+    def ml_g(self, p=None, amax=None, nu=None, abundances=None, photon_list=None, iphotons=None):
+        if photon_list is not None:
+            p = wp.to_torch(photon_list.p)
+            amax = wp.to_torch(photon_list.amax)
+            if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
+
+            if nu is None:
+                nu = wp.to_torch(photon_list.frequency)
+
+                if iphotons is not None:
+                    nu = nu[iphotons]
+                    p = p[iphotons]
+                    amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
+            else:
+                if nu.size(0) != p.size(0):
+                    p = p[iphotons]
+                    amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
+
+            abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
+
+        if amax is not None:
+            log10_amax = torch.log10(amax)
+
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(nu),)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
+
+        g = self.g_y_scaler.inverse_transform(self.g_model(self.g_x_scaler.transform(samples))).detach().flatten()
+
+        return g
+
+    def prepare_data_g(self):
+        return self.prepare_data_opacity(model="g")
+
+    def update_photon_opacities(self, photon_list, iphotons, grid=None, inu=None):
+        nphotons = iphotons.size(0)
+
+        if grid is not None and inu is not None:
+            wp.launch(kernel=self.set_photon_opacities_grid,
+                      dim=(nphotons,),
+                      inputs=[photon_list, grid, inu, iphotons])
+        else:
+            wp.launch(kernel=self.set_photon_opacities,
+                      dim=(nphotons,),
+                      inputs=[photon_list, 
+                              self.ml_kabs(photon_list=photon_list, iphotons=iphotons), 
+                              self.ml_ksca(photon_list=photon_list, iphotons=iphotons), 
+                              self.ml_g(photon_list=photon_list, iphotons=iphotons),
+                              iphotons])
+
+    @wp.kernel
+    def set_photon_opacities(photon_list: PhotonList,
+                          kabs: wp.array(dtype=float),
+                          ksca: wp.array(dtype=float),
+                          g: wp.array(dtype=float), 
+                          iphotons: wp.array(dtype=int)): # pragma: no cover
+        i = wp.tid()
+        ip = iphotons[i]
+
+        photon_list.kabs[ip] = kabs[i]
+        photon_list.ksca[ip] = ksca[i]
+        photon_list.g[ip] = g[i]
+        photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i])
+
+        photon_list.opacities_out_of_date[ip] = False
+
+    @wp.kernel
+    def set_photon_opacities_grid(photon_list: PhotonList,
+                                  grid: GridStruct,
+                                  inu: int,
+                                  iphotons: wp.array(dtype=int)): # pragma: no cover
+        ip = iphotons[wp.tid()]
+
+        ix, iy, iz = photon_list.indices[ip][0], photon_list.indices[ip][1], photon_list.indices[ip][2]
+
+        photon_list.kabs[ip] = grid.kabs[inu, ix, iy, iz]
+        photon_list.ksca[ip] = grid.ksca[inu, ix, iy, iz]
+        photon_list.g[ip] = grid.g[inu, ix, iy, iz]
+        photon_list.albedo[ip] = photon_list.ksca[ip] / (photon_list.kabs[ip] + photon_list.ksca[ip])
+
+    def set_grid_opacities(self, grid, frequency):
+        super().set_grid_opacities(grid, frequency)
+
+        p = wp.to_torch(grid.p)
+        shape = p.shape
+        p = p.flatten()
+        amax = wp.to_torch(grid.amax).flatten()
+        abundances = tuple([wp.to_torch(grid.dust_abundances)[i].flatten() for i in range(len(self.abundances))])
+
+        g = [self.ml_g(p=p, 
+                       amax=amax, 
+                       abundances=abundances,
+                       nu=torch.ones(np.prod(shape), dtype=torch.float32, 
+                                     device=wp.device_to_torch(wp.get_device())) * \
+                                        f.to(u.GHz).value) for f in frequency]
+
+        grid.g = wp.from_torch(torch.concatenate(g).reshape((len(frequency),) + shape))
+
+    def state_dict(self):
+        state_dict = super().state_dict()
+
+        state_dict["dust_properties"]["g"] = self.g
+
+        if hasattr(self, "g_model"):
+            state_dict["g_state_dict"] = self.g_model.state_dict()
+            state_dict["g_x_scaler"] = self.g_x_scaler.state_dict()
+            state_dict["g_y_scaler"] = self.g_y_scaler.state_dict()
+
+        return state_dict
+
+class GeneralDust(Dust):
+    def __init__(self, lam=None, kabs=None, ksca=None, scattering_phase_function=None, theta=None, amax=None, 
+                 p=None, abundances=(), device="cpu", ntemperatures=1000):
+        """
+        Initialize the General dust model.
+
+        Parameters
+        ----------
+        lam : numpy.ndarray
+            The wavelengths to use for the dust properties.
+        kabs : numpy.ndarray
+            The absorption opacities to use for the dust properties.
+        ksca : numpy.ndarray
+            The scattering opacities to use for the dust properties.
+        scattering_phase_function : numpy.ndarray
+            The scattering phase function to use for the dust properties, as a function of theta.
+        theta : numpy.ndarray
+            The angles at which the scattering phase function is tabulated.
+        amax : float
+            The maximum grain size to use for the dust properties.
+        p : float
+            The power-law index for the grain size distribution.
+        device : str
+            The device to place the dust properties on ("cpu" or "cuda").
+        """
+        super().__init__(lam=lam, kabs=kabs, ksca=ksca, amax=amax, p=p, abundances=abundances, device=device, 
+                         ntemperatures=ntemperatures)
+
+        # Ensure that the scattering phase function is normalized for each combination of p, amax, and nu.
+
+        scattering_phase_function = -1. * scattering_phase_function / \
+                                    np.expand_dims(scipy.integrate.trapezoid(scattering_phase_function, 
+                                                                             np.cos(theta), axis=-1), axis=-1)
+
+        self.scattering_phase_function = scattering_phase_function
+        self.theta = theta
+
+    def to_device(self, device):
+        super().to_device(device)
+
+    def scatter(self, photon_list, iphotons):
+        nphotons = iphotons.size(0)
+
+        p = wp.to_torch(photon_list.p)
+        amax = wp.to_torch(photon_list.amax)
+        frequency = wp.to_torch(photon_list.frequency)
+        if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
+        if iphotons is not None:
+            p = p[iphotons]
+            amax = amax[iphotons]
+            frequency = frequency[iphotons]
+            if photon_list.dust_abundances is not None:
+                abundances = abundances[iphotons]
+
+        abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
+            
+        nphotons = iphotons.size(0)
+        ksi = torch.rand(int(nphotons), device=wp.device_to_torch(wp.get_device()), dtype=torch.float32)
+        ksi = torch.clamp(torch.arctanh(2*ksi - 1.), min=-8.6643, max=8.6643)
+
+        if amax is not None:
+            log10_amax = torch.log10(amax)
+
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(frequency), ksi)
+
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
+
+        test_x = self.random_direction_x_scaler.transform(samples)
+
+        theta = self.random_direction_y_scaler.inverse_transform(self.random_direction_model(test_x).detach()).flatten()
+
+        wp.launch(kernel=self.random_direction,
+                  dim=(nphotons,),
+                  inputs=[photon_list.direction,
+                          wp.from_torch(theta),
+                          iphotons, 
+                          np.random.randint(0, 100000)])
+    
+    @wp.kernel
+    def random_direction(direction: wp.array(dtype=wp.vec3),
+                         theta: wp.array(dtype=float),
+                         iphotons: wp.array(dtype=int),
+                         seed: int): # pragma: no cover
+        i = wp.tid()
+        ip = iphotons[i]
+
+        rng = wp.rand_init(seed, i)
+
+        phi = 2.*np.pi*wp.randf(rng)
+
+        rpy_quat1 = wp.quat_rpy(phi, 0., 0.)
+        rpy_quat2 = wp.quat_rpy(0., theta[i], 0.)
+        direction_quat = wp.quat_between_vectors(wp.vec3(1., 0., 0.), direction[ip])
+        total_quat = direction_quat * rpy_quat1 * rpy_quat2
+
+        direction[ip] = wp.quat_rotate(total_quat, wp.vec3(1., 0., 0.))
+
+    def update_photon_scattering_phase_function(self, photon_list, direction, iphotons):
+        nphotons = iphotons.size(0)
+
+        theta = torch.acos((wp.to_torch(photon_list.direction)[iphotons] * torch.tensor(wp.array(direction))).sum(axis=1))
+
+        scattering_phase_function = self.ml_scattering_phase_function(photon_list=photon_list, iphotons=iphotons, theta=theta)
+
+        wp.launch(kernel=self.scattering_phase_function_general_dust_wp,
+                  dim=(nphotons,),
+                  inputs=[photon_list,
+                          wp.from_torch(scattering_phase_function),
+                          iphotons])
+
+    @wp.kernel
+    def scattering_phase_function_general_dust_wp(photon_list: PhotonList,
+                                                  scattering_phase_function: wp.array(dtype=float),
+                                                  iphotons: wp.array(dtype=int)): # pragma: no cover
+
+        i = wp.tid()
+        ip = iphotons[i]
+
+        photon_list.scattering_phase_function[ip] = 2. * scattering_phase_function[i]
+
+    def ml_scattering_phase_function(self, p=None, amax=None, nu=None, theta=None, photon_list=None, iphotons=None):
+        if photon_list is not None:
+            p = wp.to_torch(photon_list.p)
+            amax = wp.to_torch(photon_list.amax)
+            if photon_list.dust_abundances is not None:
+                abundances = wp.to_torch(photon_list.dust_abundances)
+
+            if nu is None:
+                nu = wp.to_torch(photon_list.frequency)
+
+                if iphotons is not None:
+                    nu = nu[iphotons]
+                    p = p[iphotons]
+                    amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
+            else:
+                if nu.size(0) != p.size(0):
+                    p = p[iphotons]
+                    amax = amax[iphotons]
+                    if abundances is not None:
+                        abundances = abundances[iphotons]
+
+            abundances = tuple([abundances[:,i] for i in range(len(self.abundances))])
+
+        if amax is not None:
+            log10_amax = torch.log10(amax)
+
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances" and abundances is not None:
+                samples += abundances
+            else:
+                samples += (eval(dim),)
+        samples += (torch.log10(nu), theta)
+        samples = torch.transpose(torch.vstack(samples), 0, 1)
+
+        scattering_phase_function = 10.**self.scattering_phase_function_y_scaler.inverse_transform(
+            self.scattering_phase_function_model(self.scattering_phase_function_x_scaler.transform(samples))).\
+                detach().flatten()
+
+        return scattering_phase_function
+
+    def learn(self, model="random_nu", **kwargs):
+        if model == "scattering_phase_function":
+            self.input_size, self.output_size = self.ndims + 2, 1
+            self.model_type = "MLP"
+        elif model == "random_direction":
+            self.input_size, self.output_size = self.ndims + 2, 1
+            self.model_type = "MLP"
+
+        super().learn(model=model, **kwargs)
+
+    def prepare_data_scattering_phase_function(self):
+        samples = np.repeat(np.repeat(np.expand_dims(np.moveaxis(self.samples, 0, 1), (-1, -2)), self.lam.size, axis=-2), self.theta.size, axis=-1)
+        self.original_indices = np.tile(np.expand_dims(np.arange(self.samples.shape[0]), axis=(-1, -2)), (1, self.nu.size, self.theta.size)).flatten()
+
+        samples = np.concat((samples, 
+                             np.tile(np.expand_dims(np.log10(self.nu.to(u.GHz).value), 
+                                                    axis=(0,1,-1)), 
+                                                    (1, samples.shape[1], 1, self.theta.size)),
+                             np.tile(np.expand_dims(self.theta.to(u.radian).value, 
+                                                    axis=(0,1,2)), 
+                                                    (1, samples.shape[1], self.lam.size, 1))), axis=0)
+        samples = samples.reshape((samples.shape[0], -1)).T
+
+        targets = np.log10(self.scattering_phase_function.flatten())
+
+        return samples, targets
+
+    def prepare_data_random_direction(self):
+        ksi = -1. * scipy.integrate.cumulative_trapezoid(self.scattering_phase_function, np.cos(self.theta), initial=0, axis=-1)
+
+        samples = np.repeat(np.repeat(np.expand_dims(np.moveaxis(self.samples, 0, 1), (-1, -2)), self.lam.size, axis=-2), self.theta.size, axis=-1)
+        self.original_indices = np.tile(np.expand_dims(np.arange(self.samples.shape[0]), axis=(-1, -2)), (1, self.nu.size, self.theta.size)).flatten()
+        
+        samples = np.concat((samples, 
+                             np.tile(np.expand_dims(np.log10(self.nu.to(u.GHz).value), 
+                                                    axis=(0,1,-1)), 
+                                                    (1, samples.shape[1], 1, self.theta.size)),
+                             np.expand_dims(ksi, axis=(0,))), axis=0)
+
+        targets = np.tile(np.expand_dims(self.theta.to(u.radian).value, axis=(0,1,2)), (1, samples.shape[1], self.lam.size, 1))
+
+        samples = samples.reshape((samples.shape[0], -1)).T
+        targets = targets.flatten()
+        self.nsamples = samples.shape[0]
+
+        samples = samples.astype(np.float32)
+
+        good = np.logical_not(np.logical_or(samples[:,-1] < 2e-8, samples[:,-1] == 1))
+
+        samples, targets = samples[good,:], targets[good]
+        self.original_indices = self.original_indices[good]
+
+        samples[:,-1] = 2*samples[:,-1] - 1
+        samples[:,-1] = np.arctanh(samples[:,-1])
+
+        return samples, targets
+
+    def plot_scattering_phase_function_model(self):
+        import matplotlib.pyplot as plt
+
+        index_samples = np.random.randint(0, self.samples.shape[0], 1)[0]
+
+        if "log10_amax" in self.dims:
+            log10_amax = np.repeat(np.log10(self.amax[index_samples].to(u.cm).value), self.theta.size)
+        if "p" in self.dims:
+            p = np.repeat(self.p[index_samples], self.theta.size)
+        if "abundances" in self.dims:
+            abundances = tuple([np.repeat(a[index_samples], self.theta.size) for a in self.abundances])
+
+        index_nu = np.random.randint(0, self.nu.size, 1)[0]
+        
+        log10_lam = np.repeat(np.log10(self.lam[index_nu].value), self.theta.size)
+        log10_nu = np.repeat(np.log10(self.nu[index_nu].to(u.GHz).value), self.theta.size)
+
+        interpolated = np.log10(self.scattering_phase_function[index_samples, index_nu, :])
+
+        print_str = ""
+        for dim in self.dims:
+            if dim == "abundances":
+                print_str += f"{dim}: {[abundances[i][0] for i in range(len(abundances))]}, "
+            else:
+                print_str += f"{dim}: {locals()[dim][0]}, "
+        print(print_str)
+
+        samples = ()
+        for dim in self.dims:
+            if dim == "abundances":
+                samples += abundances
+            else:
+                samples += (locals()[dim],)
+        
+        samples = np.vstack(samples + (log10_nu, self.theta.to(u.radian).value)).T
+        plot_x = self.theta
+
+        nned = self.scattering_phase_function_y_scaler.inverse_transform(self.scattering_phase_function_model(self.scattering_phase_function_x_scaler.transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy()
+
+        plt.plot(plot_x, interpolated)
+        plt.plot(plot_x, nned)
+        plt.show()
+
+    def state_dict(self):
+        state_dict = super().state_dict()
+
+        state_dict["dust_properties"]["scattering_phase_function"] = self.scattering_phase_function
+        state_dict["dust_properties"]["theta"] = self.theta
+
+        if hasattr(self, "scattering_phase_function_model"):
+            state_dict["scattering_phase_function_state_dict"] = self.scattering_phase_function_model.state_dict()
+            state_dict["scattering_phase_function_x_scaler"] = self.scattering_phase_function_x_scaler.state_dict()
+            state_dict["scattering_phase_function_y_scaler"] = self.scattering_phase_function_y_scaler.state_dict()
+
+        if hasattr(self, "random_direction_model"):
+            state_dict["random_direction_state_dict"] = self.random_direction_model.state_dict()
+            state_dict["random_direction_x_scaler"] = self.random_direction_x_scaler.state_dict()
+            state_dict["random_direction_y_scaler"] = self.random_direction_y_scaler.state_dict()
+
+        return state_dict
+
+def suggest_opacity_sampling(nsamples, p_range=None, amax_range=None, n_dust_subspecies=1, mode='lhs'):
+    """
+    Suggest samples for learning the opacity as a function of the dust properties using Latin Hypercube sampling.
+
+    Parameters
+    ----------
+    nsamples : int
+        The number of samples to generate.
+    p_range : tuple
+        The range of power-law indices to sample from. If None, do not sample over power-law index.
+    amax_range : tuple
+        The range of maximum dust grain sizes to sample from. If None, do not sample over maximum
+        dust grain size.
+    n_dust_subspecies: int
+        The number of component dust species whose abundances may vary. Note that the returned samples will
+        have n_dust_subspecies - 1 abundance samples because the abundances must sum to 1, and the value of the
+        last species abundance is implicit.
+
+    Returns
+    -------
+    samples : numpy.ndarray
+        An array of shape (nsamples, ndims) where ndims is the number of dimensions sampled over (i.e. 1 for each of 
+        p and amax if they are not None, plus n_dust_subspecies - 1 for the dust species abundances).
+    """
+    ndims = 0
+    if p_range is not None:
+        ndims += 1
+    if amax_range is not None:
+        ndims += 1
+    ndims += n_dust_subspecies - 1
+
+    if mode == 'lhs':
+        sampler = scipy.stats.qmc.LatinHypercube(d=ndims)
+        samples = sampler.random(nsamples)
+    elif mode == 'random':
+        samples = np.random.rand(nsamples, ndims)
+    else:
+        raise ValueError("Invalid mode. Must be either 'lhs' or 'random'.")
+
+    index = 0
+    if p_range is not None:
+        samples[:,index] = samples[:,index] * (p_range[1] - p_range[0]) + p_range[0]
+        index += 1
+    if amax_range is not None:
+        samples[:,index] = 10.**(samples[:,index] * (np.log10(amax_range[1].to(u.cm).value) - np.log10(amax_range[0].to(u.cm).value)) + np.log10(amax_range[0].to(u.cm).value))
+        index += 1
+    
+    for i in range(1, n_dust_subspecies-1):
+        samples[:, index+i] = (1. - samples[:, index:index+i].sum(axis=1)) * samples[:, index+i]
+
+    return samples
 
 def load(filename, device="cpu"):
     """
@@ -1034,16 +1754,22 @@ def load(filename, device="cpu"):
     elif isinstance(filename, Dust):
         state_dict = filename.state_dict()
 
-    d = Dust(**state_dict["dust_properties"], device=device)
+    if "g" in state_dict["dust_properties"]:
+        d = HenyeyGreensteinDust(**state_dict["dust_properties"], device=device)
+    elif "scattering_phase_function" in state_dict["dust_properties"]:
+        d = GeneralDust(**state_dict["dust_properties"], device=device)
+    else:
+        d = IsotropicDust(**state_dict["dust_properties"], device=device)
 
-    for attr in ["kabs", "ksca", "pmo", "random_nu"]:
+    for attr in ["kabs", "ksca", "pmo", "random_nu", "g", "scattering_phase_function"]:
         if f"{attr}_state_dict" in state_dict:
-            if attr == "random_nu":
-                input_size = 4
+            if attr in ["random_nu", "scattering_phase_function"]:
+                input_size = d.ndims + 2
             else:
-                input_size = 3
+                input_size = d.ndims + 1
+
             hidden_units = [state_dict[f'{attr}_state_dict'][key].shape[0] for key in state_dict[f'{attr}_state_dict'] if 'bias' in key][0:-1]
-            d.initialize_model(model=attr, input_size=input_size, output_size=1, hidden_units=hidden_units)
+            d.initialize_model(model=attr, model_type="MLP", input_size=input_size, output_size=1, hidden_units=hidden_units)
 
             getattr(d, f'{attr}_model').load_state_dict(state_dict[f'{attr}_state_dict'])
             setattr(d, f'{attr}_x_scaler', StandardScaler())
@@ -1051,10 +1777,22 @@ def load(filename, device="cpu"):
             setattr(d, f'{attr}_y_scaler', StandardScaler())
             getattr(d, f'{attr}_y_scaler').load_state_dict(state_dict[f"{attr}_y_scaler"])
 
+    if "random_direction_state_dict" in state_dict:
+        hidden_units = (tuple([state_dict['random_direction_state_dict'][key].size(1) for key in state_dict['random_direction_state_dict'] if '0.hyper' in key and 'weight' in key and '0.weight' not in key]),) * len([state_dict['random_direction_state_dict'][key].size(0) for key in state_dict['random_direction_state_dict'] if '0.weight' in key])
+
+        d.initialize_model(model="random_direction", model_type="flow", input_size=1, output_size=3, hidden_units=hidden_units)
+
+        d.random_direction_model.load_state_dict(state_dict['random_direction_state_dict'])
+
+        d.random_direction_x_scaler = StandardScaler()
+        d.random_direction_x_scaler.load_state_dict(state_dict["random_direction_x_scaler"])
+        d.random_direction_y_scaler = StandardScaler()
+        d.random_direction_y_scaler.load_state_dict(state_dict["random_direction_y_scaler"])
+
     if "ml_step_state_dict" in state_dict:
         hidden_units = (tuple([state_dict['ml_step_state_dict'][key].size(1) for key in state_dict['ml_step_state_dict'] if '0.hyper' in key and 'weight' in key and '0.weight' not in key]),) * len([state_dict['ml_step_state_dict'][key].size(0) for key in state_dict['ml_step_state_dict'] if '0.weight' in key])
 
-        d.initialize_model(model="ml_step", input_size=7, output_size=5, hidden_units=hidden_units)
+        d.initialize_model(model="ml_step", model_type="flow", input_size=7, output_size=5, hidden_units=hidden_units)
 
         d.ml_step_model.load_state_dict(state_dict['ml_step_state_dict'])
 
@@ -1183,4 +1921,4 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
         if hasattr(self.model, "condition"):
             return self.condition(y).sample()
         else:
-            return self(x)
\ No newline at end of file
+            return self(x)
diff --git a/pinballrt/grids.py b/pinballrt/grids.py
index 177f268..25f69b9 100644
--- a/pinballrt/grids.py
+++ b/pinballrt/grids.py
@@ -10,7 +10,7 @@
 import time
 from  tqdm.auto import tqdm
 
-from .utils import GridStruct, EPSILON, equal, equal_zero, planck_function
+from .utils import GridStruct, EPSILON, equal, equal_zero, planck_function, random_direction
 
 class Grid:
     def __init__(self, _w1, _w2, _w3, device='cpu'):
@@ -33,11 +33,11 @@ def __getstate__(self):
                 
         new_dict = {}
         for entry in state['grid'].__dict__:
-            if isinstance(getattr(self.grid, entry), wp.types.array):
+            if wp.types.is_array(getattr(self.grid, entry)):
                 new_dict[entry] = getattr(self.grid, entry).numpy()
             elif isinstance(getattr(self.grid, entry), type(None)):
                 new_dict[entry] = getattr(self.grid, entry)
-            elif isinstance(getattr(self.grid, entry), wp.types.bool):
+            elif isinstance(getattr(self.grid, entry), wp.bool):
                 new_dict[entry] = bool(getattr(self.grid, entry))
             elif isinstance(getattr(self.grid, entry), int):
                 new_dict[entry] = getattr(self.grid, entry)
@@ -61,7 +61,7 @@ def __setstate__(self, state):
 
         self.__dict__.update(state)
 
-    def set_physical_properties(self, density=None, dusttogasratio=0.01, dust=None, amax=None, p=None, gases=None, abundances=None, 
+    def set_physical_properties(self, density=None, dusttogasratio=0.01, dust=None, amax=None, p=None, dust_abundances=(), gases=None, abundances=None, 
                                 velocity=None, microturbulence=None):
         with wp.ScopedDevice(self.device):
             if density is not None:
@@ -94,6 +94,17 @@ def set_physical_properties(self, density=None, dusttogasratio=0.01, dust=None,
                 self.dust = dust
                 self.dust.to_device(wp.device_to_torch(wp.get_device()))
 
+            if len(dust_abundances) > 0:
+                self.grid.dust_abundances = wp.array4d(dust_abundances,  dtype=float)
+                self.n_dust_abundances = len(dust_abundances)
+            else:
+                if len(self.dust.abundances) > 0:
+                    self.grid.dust_abundances = wp.array4d(np.ones((len(self.dust.abundances),)+self.shape) * 
+                                                           len(self.dust.abundances) / (len(self.dust.abundances) + 1.), dtype=float)
+                    self.n_dust_abundances = len(self.dust.abundances)
+                else:
+                    self.n_dust_abundances = 0
+
             if gases is not None:
                 self.gases = []
                 for g in gases:
@@ -290,7 +301,8 @@ def deposit_scattering(photon_list: PhotonList,
     @wp.kernel
     def photon_cell_properties(photon_list: PhotonList,
                            grid: GridStruct,
-                           iphotons: wp.array(dtype=int)): # pragma: no cover
+                           iphotons: wp.array(dtype=int),
+                           n_dust_abundances: int): # pragma: no cover
         itemp = wp.tid()
         ip = iphotons[itemp]
 
@@ -298,40 +310,38 @@ def photon_cell_properties(photon_list: PhotonList,
 
         photon_list.temperature[ip] = grid.temperature[ix, iy, iz]
         photon_list.density[ip] = grid.dust_density[ix, iy, iz]
-        photon_list.amax[ip] = grid.amax[ix, iy, iz]
-        photon_list.p[ip] = grid.p[ix, iy, iz]
+
+        new_amax = grid.amax[ix, iy, iz]
+        new_p = grid.p[ix, iy, iz]
+
+        updated_dust_properties = False
+        if photon_list.amax[ip] != new_amax or photon_list.p[ip] != new_p:
+            updated_dust_properties = True
+        
+        photon_list.amax[ip] = new_amax
+        photon_list.p[ip] = new_p
+
+        for i in range(n_dust_abundances):
+            new_abundance = grid.dust_abundances[i, ix, iy, iz]
+
+            if photon_list.dust_abundances[ip][i] != new_abundance:
+                updated_dust_properties = True
+
+            photon_list.dust_abundances[ip][i] = new_abundance
+
+        if updated_dust_properties:
+            photon_list.opacities_out_of_date[ip] = True
 
     @wp.kernel
     def update_frequency(photon_list: PhotonList,
                          frequency: wp.array(dtype=float),
-                         kabs: wp.array(dtype=float),
-                         ksca: wp.array(dtype=float),
                          iphotons: wp.array(dtype=int)): # pragma: no cover
         
         i = wp.tid()
         ip = iphotons[i]
 
         photon_list.frequency[ip] = frequency[i]
-        photon_list.kabs[ip] = kabs[i]
-        photon_list.ksca[ip] = ksca[i]
-        photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i])
-
-    @wp.kernel
-    def random_direction(direction: wp.array(dtype=wp.vec3),
-                         iphotons: wp.array(dtype=int),
-                         seed: int): # pragma: no cover
-        i = wp.tid()
-        ip = iphotons[i]
-
-        rng = wp.rand_init(seed, i)
-
-        cost = -1. + 2.*wp.randf(rng)
-        sint = wp.sqrt(1.-cost**2.)
-        phi = 2.*np.pi*wp.randf(rng)
-
-        direction[ip][0] = sint*np.cos(phi)
-        direction[ip][1] = sint*np.sin(phi)
-        direction[ip][2] = cost
+        photon_list.opacities_out_of_date[ip] = True
 
     @wp.kernel
     def random_tau(photon_list: PhotonList,
@@ -355,13 +365,9 @@ def random_absorb(photon_list: PhotonList,
 
         photon_list.absorb[ip] = wp.randf(rng) > photon_list.albedo[ip]
 
-    def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons, scattering=False, learning=False):
+    def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons, iscatter, scattering=False, learning=False):
         nphotons = iphotons.size(0)
 
-        wp.launch(kernel=self.random_direction,
-                  dim=(nphotons,),
-                  inputs=[photon_list.direction, iphotons, np.random.randint(0, 100000)])
-
         t1 = time.time()
         nabsorb = iabsorb.size(0)
         #if not scattering and nabsorb > 0:
@@ -372,6 +378,12 @@ def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons,
         t2 = time.time()
         photon_temperature_time = t2 - t1
 
+        wp.launch(kernel=random_direction,
+                  dim=(nabsorb,),
+                  inputs=[photon_list.direction, iabsorb, np.random.randint(0, 100000)])
+
+        self.dust.scatter(photon_list, iscatter)
+
         t1 = time.time()
         if not scattering and nabsorb > 0:
             new_frequency = self.dust.random_nu(photon_list, subset=absorb)
@@ -382,9 +394,9 @@ def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons,
         if not scattering and nabsorb > 0:
             wp.launch(kernel=self.update_frequency,
                       dim=(nabsorb,),
-                      inputs=[photon_list, new_frequency, 
-                              self.dust.ml_kabs(photon_list=photon_list, nu=wp.to_torch(new_frequency), iphotons=iabsorb), 
-                              self.dust.ml_ksca(photon_list=photon_list, nu=wp.to_torch(new_frequency), iphotons=iabsorb), iabsorb])
+                      inputs=[photon_list, new_frequency, iabsorb])
+            
+            #self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iabsorb)
         t2 = time.time()
         dust_interpolation_time = t2 - t1
     
@@ -420,7 +432,9 @@ def update_grid(self, timing={}):
 
                 t1 = time.time()
                 planck_mean_opacity = self.dust.ml_planck_mean_opacity(torch.tensor(self.grid.p.numpy().flatten()), torch.tensor(self.grid.amax.numpy().flatten()), 
-                                                                       torch.tensor(old_temperature.flatten(), dtype=torch.float32)).numpy().reshape(self.shape)
+                                                                       torch.tensor(old_temperature.flatten(), dtype=torch.float32), 
+                                                                       abundances=tuple([torch.tensor(self.grid.dust_abundances.numpy()[i].flatten(), dtype=torch.float32) for 
+                                                                                         i in range(self.n_dust_abundances)])).numpy().reshape(self.shape)
                 t2 = time.time()
                 pmo_time += t2 - t1
 
@@ -512,7 +526,9 @@ def ml_step(self, photon_list, s, iphotons):
 
         wp.launch(kernel=self.update_frequency,
                   dim=(nphotons,),
-                  inputs=[photon_list, wp.from_torch(frequency), self.dust.ml_kabs(photon_list=photon_list, nu=wp.from_torch(frequency, iphotons=iphotons)), self.dust.ml_ksca(photon_list=photon_list, nu=wp.from_torch(frequency, iphotons=iphotons)), iphotons])
+                  inputs=[photon_list, wp.from_torch(frequency), iphotons])
+
+        self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons)
 
         wp.launch(kernel=self.ml_rotate_direction,
                   dim=(nphotons,),
@@ -524,19 +540,7 @@ def ml_step(self, photon_list, s, iphotons):
         
         return wp.from_torch(direction_yaw), wp.from_torch(direction_pitch), wp.from_torch(direction_roll)
 
-    @wp.kernel
-    def set_photon_opacities(photon_list: PhotonList,
-                          kabs: wp.array(dtype=float),
-                          ksca: wp.array(dtype=float),
-                          iphotons: wp.array(dtype=int)): # pragma: no cover
-        i = wp.tid()
-        ip = iphotons[i]
-
-        photon_list.kabs[ip] = kabs[i]
-        photon_list.ksca[ip] = ksca[i]
-        photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i])
-
-    def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning=False, debug=False, timing={}, position=0):
+    def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning=False, debug=False, timing={}, position=0, time_limit=np.inf):
         with wp.ScopedDevice(self.device):
             nphotons = photon_list.position.numpy().shape[0]
             iphotons_original = torch.arange(nphotons, dtype=torch.int32, device=wp.device_to_torch(wp.get_device()))
@@ -567,8 +571,10 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning
 
             photon_list.kabs = wp.zeros(nphotons, dtype=float)
             photon_list.ksca = wp.zeros(nphotons, dtype=float)
+            photon_list.g = wp.zeros(nphotons, dtype=float)
             photon_list.albedo = wp.zeros(nphotons, dtype=float)
             photon_list.absorb = wp.zeros(nphotons, dtype=bool)
+            photon_list.opacities_out_of_date = wp.zeros(nphotons, dtype=bool)
 
             progress_bar = tqdm(total=nphotons, position=position, leave=True)
 
@@ -583,12 +589,7 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning
             nphotons = iphotons.size(0)
 
             t1 = time.time()
-            wp.launch(kernel=self.set_photon_opacities,
-                      dim=(nphotons,),
-                      inputs=[photon_list, 
-                              wp.from_torch(self.dust.ml_kabs(photon_list=photon_list, iphotons=iphotons)),
-                              wp.from_torch(self.dust.ml_ksca(photon_list=photon_list, iphotons=iphotons)),
-                              iphotons])
+            self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons)
             t2 = time.time()
             dust_interpolation_time += t2 - t1
 
@@ -598,7 +599,8 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning
 
             count = 0
             nphotons_done = 0
-            while nphotons > 0:
+            start_time = time.time()
+            while nphotons > 0 and time.time() - start_time < time_limit:
                 #print(nphotons)
                 count += 1
 
@@ -707,7 +709,9 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning
                 interaction_indices = iphotons_original[interaction]
                 absorb = torch.logical_and(interaction, wp.to_torch(photon_list.absorb))
                 absorb_indices = iphotons_original[absorb]
-                tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, learning=learning)
+                scatter = torch.logical_and(interaction, wp.to_torch(photon_list.absorb) == False)
+                scatter_indices = iphotons_original[scatter]
+                tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, scatter_indices, learning=learning)
                 t2 = time.time()
                 absorb_time += t2 - t1 - tmp_dust_interpolation_time - tmp_photon_loc_time
                 absorb_random_nu_time += tmp_absorb_random_nu_time
@@ -727,15 +731,12 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning
                     if not learning:
                         wp.launch(kernel=self.photon_cell_properties,
                                   dim=(nphotons,),
-                                  inputs=[photon_list, self.grid, iphotons])
+                                  inputs=[photon_list, self.grid, iphotons, self.n_dust_abundances])
                     
                     t1 = time.time()
-                    wp.launch(kernel=self.set_photon_opacities,
-                            dim=(nphotons,),
-                            inputs=[photon_list, 
-                                    wp.from_torch(self.dust.ml_kabs(photon_list=photon_list, iphotons=iphotons)),
-                                    wp.from_torch(self.dust.ml_ksca(photon_list=photon_list, iphotons=iphotons)),
-                                    iphotons])
+                    iphotons_opacities = iphotons_original[torch.logical_and(wp.to_torch(photon_list.in_grid), 
+                                                                             wp.to_torch(photon_list.opacities_out_of_date))]
+                    self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons_opacities)
                     t2 = time.time()
                     dust_interpolation_time += t2 - t1
 
@@ -756,37 +757,9 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning
 
     def set_grid_opacities(self, frequency):
         with wp.ScopedDevice(self.device):
-            p = wp.to_torch(self.grid.p).flatten()
-            amax = wp.to_torch(self.grid.amax).flatten()
-
-            kabs = [self.dust.ml_kabs(p=p, amax=amax, nu=torch.ones(np.prod(self.shape), 
-                                                                    dtype=torch.float32, 
-                                                                    device=wp.device_to_torch(wp.get_device())) * \
-                                                                        f.to(u.GHz).value) for f in frequency]
-
-            self.grid.kabs = wp.from_torch(torch.concatenate(kabs).reshape((len(frequency),) + self.shape))
+            self.dust.set_grid_opacities(self.grid, frequency)
 
-            ksca = [self.dust.ml_ksca(p=p, amax=amax, nu=torch.ones(np.prod(self.shape), 
-                                                                    dtype=torch.float32, 
-                                                                    device=wp.device_to_torch(wp.get_device())) * \
-                                                                        f.to(u.GHz).value) for f in frequency]
-            
-            self.grid.ksca = wp.from_torch(torch.concatenate(ksca).reshape((len(frequency),) +self.shape))
-
-    @wp.kernel
-    def update_photon_opacities(photon_list: PhotonList,
-                                grid: GridStruct,
-                                inu: int,
-                                iphotons: wp.array(dtype=int)): # pragma: no cover
-        ip = iphotons[wp.tid()]
-
-        ix, iy, iz = photon_list.indices[ip][0], photon_list.indices[ip][1], photon_list.indices[ip][2]
-
-        photon_list.kabs[ip] = grid.kabs[inu, ix, iy, iz]
-        photon_list.ksca[ip] = grid.ksca[inu, ix, iy, iz]
-        photon_list.albedo[ip] = photon_list.ksca[ip] / (photon_list.kabs[ip] + photon_list.ksca[ip])
-
-    def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=False, timing={}, position=0):
+    def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, camera_direction: wp.vec3, debug=False, timing={}, position=0):
         with wp.ScopedDevice(self.device):
             nphotons = photon_list.position.numpy().shape[0]
             iphotons_original = torch.arange(nphotons, dtype=torch.int32, device=wp.device_to_torch(wp.get_device()))
@@ -811,8 +784,11 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=
 
             photon_list.kabs = wp.zeros(nphotons, dtype=float)
             photon_list.ksca = wp.zeros(nphotons, dtype=float)
+            photon_list.g = wp.zeros(nphotons, dtype=float)
             photon_list.albedo = wp.zeros(nphotons, dtype=float)
             photon_list.absorb = wp.zeros(nphotons, dtype=bool)
+            photon_list.scattering_phase_function = wp.zeros(nphotons, dtype=float)
+            photon_list.opacities_out_of_date = wp.zeros(nphotons, dtype=bool)
 
             progress_bar = tqdm(total=nphotons, position=position, leave=True)
 
@@ -827,9 +803,7 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=
             nphotons = iphotons.size(0)
 
             t1 = time.time()
-            wp.launch(kernel=self.update_photon_opacities, 
-                      dim=(nphotons,),
-                      inputs=[photon_list, self.grid, inu, iphotons])
+            self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons, grid=self.grid, inu=inu)
             t2 = time.time()
             dust_interpolation_time += t2 - t1
 
@@ -858,6 +832,10 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=
 
                 s = torch.minimum(wp.to_torch(s1), wp.to_torch(s2))
 
+                self.dust.update_photon_scattering_phase_function(photon_list=photon_list, 
+                                                                  direction=camera_direction, 
+                                                                  iphotons=iphotons)
+
                 t1 = time.time()
                 wp.launch(kernel=self.deposit_scattering,
                           dim=(nphotons,),
@@ -899,7 +877,9 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=
                 interaction_indices = iphotons_original[interaction]
                 absorb = torch.logical_and(interaction, wp.to_torch(photon_list.absorb))
                 absorb_indices = iphotons_original[absorb]
-                tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, scattering=True)
+                scatter = torch.logical_and(interaction, wp.to_torch(photon_list.absorb) == False)
+                scatter_indices = iphotons_original[scatter]
+                tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, scatter_indices, scattering=True)
                 t2 = time.time()
                 absorb_time += t2 - t1 - tmp_photon_loc_time
                 #absorb_time += tmp_time
@@ -915,12 +895,12 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=
                 if nphotons > 0:
                     wp.launch(kernel=self.photon_cell_properties,
                               dim=(nphotons,),
-                              inputs=[photon_list, self.grid, iphotons])
+                              inputs=[photon_list, self.grid, iphotons, self.n_dust_abundances])
                     
                     t1 = time.time()
-                    wp.launch(kernel=self.update_photon_opacities, 
-                            dim=(nphotons,),
-                            inputs=[photon_list, self.grid, inu, iphotons])
+                    iphotons_opacities = iphotons_original[torch.logical_and(wp.to_torch(photon_list.in_grid), 
+                                                                             wp.to_torch(photon_list.opacities_out_of_date))]
+                    self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons_opacities, grid=self.grid, inu=inu)
                     t2 = time.time()
                     dust_interpolation_time += t2 - t1
 
@@ -1087,18 +1067,6 @@ def reduce_source_intensity(ray_list: PhotonList,
 
         ray_list.intensity[ir, inu] = ray_list.intensity[ir, inu] * wp.exp(-s[ir] * ray_list.kext[ir, inu] * grid.dust_density[ix, iy, iz])
 
-    @wp.kernel
-    def set_ray_opacities(ray_list: PhotonList,
-                          kext: wp.array2d(dtype=float),
-                          albedo: wp.array2d(dtype=float),
-                          irays: wp.array(dtype=int)): # pragma: no cover
-        
-        iray, inu = wp.tid()
-        ir = irays[iray]
-
-        ray_list.kext[ir, inu] = kext[iray, inu]
-        ray_list.ray_albedo[ir, inu] = albedo[iray, inu]
-
     @wp.kernel
     def set_ray_opacities_grid(ray_list: PhotonList,
                                grid: GridStruct,
@@ -1133,7 +1101,7 @@ def propagate_rays(self, ray_list: PhotonList, frequency, pixel_size):
 
             wp.launch(kernel=self.photon_cell_properties,
                       dim=(nrays,),
-                      inputs=[ray_list, self.grid, iray])
+                      inputs=[ray_list, self.grid, iray, self.n_dust_abundances])
             
             wp.launch(kernel=self.set_ray_opacities_grid,
                       dim=(nrays, nnu),
@@ -1179,7 +1147,7 @@ def propagate_rays(self, ray_list: PhotonList, frequency, pixel_size):
                 if nrays > 0:
                     wp.launch(kernel=self.photon_cell_properties,
                               dim=(nrays,),
-                              inputs=[ray_list, self.grid, iray])
+                              inputs=[ray_list, self.grid, iray, self.n_dust_abundances])
 
                     wp.launch(kernel=self.set_ray_opacities_grid,
                               dim=(nrays, nnu),
@@ -1194,10 +1162,14 @@ def propagate_rays_from_source(self, ray_list: PhotonList, frequency):
             ray_list.in_grid = wp.zeros(nrays, dtype=bool)
 
             ray_list.frequency = wp.array(frequency, dtype=float)
+
+            if self.n_dust_abundances > 0:
+                ray_list.dust_abundances = wp.zeros((nrays, self.n_dust_abundances), dtype=float)
+            ray_list.opacities_out_of_date = wp.zeros(nrays, dtype=bool)
     
             wp.launch(kernel=self.photon_cell_properties,
                       dim=(nrays,),
-                      inputs=[ray_list, self.grid, iray])
+                      inputs=[ray_list, self.grid, iray, self.n_dust_abundances])
 
             ray_list.kext = wp.zeros((nrays, frequency.size), dtype=float)
             ray_list.ray_albedo = wp.zeros((nrays, frequency.size), dtype=float)
@@ -1234,7 +1206,7 @@ def propagate_rays_from_source(self, ray_list: PhotonList, frequency):
                 if nrays > 0:
                     wp.launch(kernel=self.photon_cell_properties,
                               dim=(nrays,),
-                              inputs=[ray_list, self.grid, iray])
+                              inputs=[ray_list, self.grid, iray, self.n_dust_abundances])
 
                     wp.launch(kernel=self.set_ray_opacities_grid,
                               dim=(nrays, nnu),
@@ -1300,11 +1272,14 @@ def emit(self, nphotons, wavelength="random", scattering=False, learning=False,
             photon_list.temperature = wp.zeros(nphotons, dtype=float)
             photon_list.amax = wp.zeros(nphotons, dtype=float)
             photon_list.p = wp.zeros(nphotons, dtype=float)
+            if self.n_dust_abundances > 0:
+                photon_list.dust_abundances = wp.zeros((nphotons, self.n_dust_abundances), dtype=float)
+            photon_list.opacities_out_of_date = wp.zeros(nphotons, dtype=bool)
 
             if not learning:
                 wp.launch(kernel=self.photon_cell_properties,
                           dim=(nphotons,),
-                          inputs=[photon_list, self.grid, iphotons])
+                          inputs=[photon_list, self.grid, iphotons, self.n_dust_abundances])
 
         return photon_list
 
@@ -1628,11 +1603,14 @@ def emit(self, nphotons, wavelength="random", scattering=False, learning=False,
             photon_list.temperature = wp.array(np.zeros(nphotons), dtype=float)
             photon_list.amax = wp.array(np.zeros(nphotons), dtype=float)
             photon_list.p = wp.array(np.zeros(nphotons), dtype=float)
+            if self.n_dust_abundances > 0:
+                photon_list.dust_abundances = wp.zeros((nphotons, self.n_dust_abundances), dtype=float)
+            photon_list.opacities_out_of_date = wp.zeros(nphotons, dtype=bool)
 
             if not learning:
                 wp.launch(kernel=self.photon_cell_properties,
                           dim=(nphotons,),
-                          inputs=[photon_list, self.grid, iphotons])
+                          inputs=[photon_list, self.grid, iphotons, self.n_dust_abundances])
 
         return photon_list
 
@@ -2108,11 +2086,14 @@ def emit(self, nphotons, wavelength="random", scattering=False, learning=False,
             photon_list.temperature = wp.array(np.zeros(nphotons), dtype=float)
             photon_list.amax = wp.array(np.zeros(nphotons), dtype=float)
             photon_list.p = wp.array(np.zeros(nphotons), dtype=float)
+            if self.n_dust_abundances > 0:
+                photon_list.dust_abundances = wp.zeros((nphotons, self.n_dust_abundances), dtype=float)
+            photon_list.opacities_out_of_date = wp.zeros(nphotons, dtype=bool)
 
             if not learning:
                 wp.launch(kernel=self.photon_cell_properties,
                           dim=(nphotons,),
-                          inputs=[photon_list, self.grid, iphotons])
+                          inputs=[photon_list, self.grid, iphotons, self.n_dust_abundances])
 
         return photon_list
     
diff --git a/pinballrt/model.py b/pinballrt/model.py
index 4dd5df9..5f49f8c 100644
--- a/pinballrt/model.py
+++ b/pinballrt/model.py
@@ -3,7 +3,7 @@
 
 from .sources import DiffuseSource, EnergySource
 from .grids import Grid
-from .dust import load, Dust
+from .dust import load
 from .gas import Gas
 from .camera import Camera
 from schwimmbad import SerialPool, MultiPool
@@ -220,6 +220,8 @@ def scattering_mc(self, nphotons, wavelengths, device="cpu", return_timing=False
         for i, wavelength in enumerate(wavelengths):
             iter_timing = {}
 
+            frequency = const.c / wavelength
+
             for source in self.grid_list[device].sources + [self.grid_list[device].grid_source]:
                 if isinstance(source, DiffuseSource):
                     source.initialize_luminosity_array(wavelength=wavelength)
@@ -234,7 +236,8 @@ def scattering_mc(self, nphotons, wavelengths, device="cpu", return_timing=False
                                        [nphotons]*njobs,
                                        [njobs]*njobs,
                                        [wavelength]*njobs,
-                                       [i]*njobs,))
+                                       [i]*njobs,
+                                       [self.camera_list[device].i_wp]*njobs))
             total_scattering = [r[0] for r in result]
             time.sleep(0.1)
             t2 = time.time()
@@ -247,11 +250,11 @@ def scattering_mc(self, nphotons, wavelengths, device="cpu", return_timing=False
                 if isinstance(source, DiffuseSource) and not isinstance(source, EnergySource):
                     total_scattering[i] += torch.tensor((source.luminosity * (self.grid.distance_unit**2 * u.Jy) * \
                                                          source.density / (4.*np.pi * u.steradian * \
-                                                                           (self.grid.grid.dust_density.numpy() * \
-                                                                            self.grid.dust.interpolate_kext(
-                                                                                self.grid.grid.p.numpy(), 
-                                                                                self.grid.grid.amax.numpy(), 
-                                                                                np.ones(self.grid.shape)*wavelength) * \
+                                                                           (wp.to_torch(self.grid.grid.dust_density) * \
+                                                                            self.grid.dust.ml_kext(
+                                                                                wp.to_torch(self.grid.grid.p).flatten(), 
+                                                                                wp.to_torch(self.grid.grid.amax).flatten(), 
+                                                                                torch.ones(self.grid.shape).flatten()*frequency.to(u.GHz).value).reshape(self.grid.shape) * \
                                                          self.grid.distance_unit**-1))).value, 
                                                          device=device)
 
@@ -325,44 +328,20 @@ def make_image(self, npix=100, pixel_size=None, channels=None, rest_frequency=No
 
         # Check which lines from the gas should be included
 
-        if include_gas:
-            self.grid.select_lines(lam)
-
-        self.grid.grid.include_dust = include_dust
-        self.grid.grid.include_gas = include_gas
-
-        # Check whether spectral is wavelength or frequency
-
-        if channels.unit.is_equivalent(u.micron):
-            lam = channels.to(u.micron)
-            nu = (const.c / channels).to(u.GHz)
-        elif channels.unit.is_equivalent(u.GHz):
-            nu = channels.to(u.GHz)
-            lam = (const.c / nu).to(u.micron)
-        elif channels.unit.is_equivalent(u.km / u.s):
-            if rest_frequency is None:
-                raise ValueError("rest_frequency must be provided when channels are in velocity units.")
-            nu = (rest_frequency * (1 - channels / const.c)).to(u.GHz)
-            lam = (const.c / nu).to(u.micron)
-        else:
-            raise ValueError("Either lam or nu must be provided.")
-
-        # Check which lines from the gas should be included
-
         if include_gas:
             self.grid.select_lines(lam)
 
         # First, run a scattering simulation to get the scattering phase function
 
+        for dev in self.camera_list:
+            self.camera_list[dev].set_orientation(incl, pa, distance)
+
+        self.grid_list[device].set_grid_opacities(nu)
         if include_dust:
-            self.grid_list[device].set_grid_opacities(nu)
             self.scattering_mc(nphotons, lam, device=device, set_grid_opacities=False)
 
         # Now set up the image proper.
 
-        for dev in self.camera_list:
-            self.camera_list[dev].set_orientation(incl, pa, distance)
-
         physical_pixel_size = (pixel_size*distance).to(self.grid.distance_unit, equivalencies=u.dimensionless_angles()).value
 
         image = xr.Dataset(
@@ -402,7 +381,7 @@ def make_image(self, npix=100, pixel_size=None, channels=None, rest_frequency=No
                                                                 [njobs]*njobs,)
                                                         ))).mean(axis=0) * u.Jy/u.steradian
 
-        intensity += source_intensity
+            intensity += source_intensity
 
         image = image.assign(intensity=(("x","y","lam"), intensity.to(u.Jy / u.steradian)))
 
@@ -448,11 +427,11 @@ def thermal_mc_task(args):
     return grid.grid.energy.numpy(), iter_timing
 
 def scattering_mc_task(args):
-    grid, position, s, nphotons, njobs, wavelength, i = args
+    grid, position, s, nphotons, njobs, wavelength, i, camera_direction = args
     seed(s.generate_state(1)[0])
     iter_timing = {}
     photon_list = grid.emit(int(nphotons / njobs), wavelength, scattering=True, timing=iter_timing)
-    grid.propagate_photons_scattering(photon_list, i, timing=iter_timing, position=position)
+    grid.propagate_photons_scattering(photon_list, i, camera_direction, timing=iter_timing, position=position)
 
     return grid.scattering, iter_timing
 
diff --git a/pinballrt/photons.py b/pinballrt/photons.py
index 37afd55..7dc5cc8 100644
--- a/pinballrt/photons.py
+++ b/pinballrt/photons.py
@@ -17,12 +17,17 @@ class PhotonList:
     alpha: wp.array(dtype=float)
     kabs: wp.array(dtype=float)
     ksca: wp.array(dtype=float)
+    g: wp.array(dtype=float)
+    scattering_phase_function: wp.array(dtype=float)
     albedo: wp.array(dtype=float)
     kext: wp.array2d(dtype=float)
     ray_albedo: wp.array2d(dtype=float)
     absorb: wp.array(dtype=bool)
     amax: wp.array(dtype=float)
     p: wp.array(dtype=float)
+    dust_abundances: wp.array2d(dtype=float)
+
+    opacities_out_of_date: wp.array(dtype=bool)
 
     tau: wp.array(dtype=float)
     total_tau_abs: wp.array(dtype=float)
diff --git a/pinballrt/sources.py b/pinballrt/sources.py
index 7f3b44c..019646e 100644
--- a/pinballrt/sources.py
+++ b/pinballrt/sources.py
@@ -19,7 +19,7 @@
 
 from pinballrt.utils import EPSILON
 from .photons import PhotonList
-from .utils import GridStruct
+from .utils import GridStruct, random_direction
 from astropy.modeling import models
 import astropy.units as u
 import astropy.constants as const
@@ -409,7 +409,7 @@ def emit(self, nphotons, distance_unit, wavelength="random", simulation="thermal
 
             photon_list.direction = wp.array(np.zeros((nphotons, 3)), dtype=wp.vec3)
             photon_list.direction_frame = wp.array(np.zeros((nphotons, 3)), dtype=wp.vec3)
-            wp.launch(kernel=self.grid.random_direction,
+            wp.launch(kernel=random_direction,
                         dim=(nphotons,),
                         inputs=[photon_list.direction, torch.arange(nphotons, dtype=torch.int32, device=wp.device_to_torch(wp.get_device())), np.random.randint(0, 100000)])
             
@@ -453,7 +453,10 @@ def initialize_luminosity_array(self, wavelength):
                                    self.grid.volume.cpu().numpy()*\
                                    self.grid.dust.ml_kabs(wp.to_torch(self.grid.grid.p).flatten(), 
                                                           wp.to_torch(self.grid.grid.amax).flatten(), 
-                                                          torch.tensor(nu.value, dtype=torch.float32, device=wp.device_to_torch(wp.get_device())).expand(np.prod(self.grid.shape))).cpu().numpy().reshape(self.grid.shape)*\
+                                                          torch.tensor(nu.value, dtype=torch.float32, device=wp.device_to_torch(wp.get_device())
+                                                                       ).expand(np.prod(self.grid.shape)), 
+                                                          abundances=tuple([wp.to_torch(self.grid.grid.dust_abundances)[i].flatten() for i in 
+                                                                            range(self.grid.n_dust_abundances)])).cpu().numpy().reshape(self.grid.shape)*\
                                    self.grid.distance_unit**2*models.BlackBody(temperature=self.grid.grid.temperature.numpy()*u.K)(nu)).to(u.au**2 * u.Jy).value
 
         self.total_lum = self.luminosity.sum()
@@ -467,9 +470,13 @@ def random_nu(self, nphotons, cell_coords):
         photon_list.temperature = wp.zeros(nphotons, dtype=float)
         photon_list.amax = wp.zeros(nphotons, dtype=float)
         photon_list.p = wp.zeros(nphotons, dtype=float)
+        if self.grid.n_dust_abundances > 0:
+            photon_list.dust_abundances = wp.zeros((nphotons, self.grid.n_dust_abundances), dtype=float)
+        photon_list.opacities_out_of_date = wp.zeros(nphotons, dtype=bool)
+
         wp.launch(kernel=self.grid.photon_cell_properties,
                     dim=(nphotons,),
-                    inputs=[photon_list, self.grid.grid, wp.array(np.arange(nphotons), dtype=int)])
+                    inputs=[photon_list, self.grid.grid, wp.array(np.arange(nphotons), dtype=int), self.grid.n_dust_abundances])
 
         return self.grid.dust.random_nu(photon_list)
 
diff --git a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_image.nc b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_image.nc
index 9868a99..10c07e7 100644
Binary files a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_image.nc and b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_image.nc differ
diff --git a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_mom0.nc b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_mom0.nc
index 11439ab..25aedb9 100644
Binary files a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_mom0.nc and b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_mom0.nc differ
diff --git a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_scattering.npz b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_scattering.npz
index 8086af1..ac70dad 100644
Binary files a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_scattering.npz and b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_scattering.npz differ
diff --git a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_temperature.npz b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_temperature.npz
index 3c69f4c..de2ed55 100644
Binary files a/pinballrt/tests/data/LogUniformSphericalGrid_E2E_temperature.npz and b/pinballrt/tests/data/LogUniformSphericalGrid_E2E_temperature.npz differ
diff --git a/pinballrt/tests/data/UniformCartesianGrid_E2E_image.nc b/pinballrt/tests/data/UniformCartesianGrid_E2E_image.nc
index 7c98971..fe0472c 100644
Binary files a/pinballrt/tests/data/UniformCartesianGrid_E2E_image.nc and b/pinballrt/tests/data/UniformCartesianGrid_E2E_image.nc differ
diff --git a/pinballrt/tests/data/UniformCartesianGrid_E2E_mom0.nc b/pinballrt/tests/data/UniformCartesianGrid_E2E_mom0.nc
index d976bae..9c6fa36 100644
Binary files a/pinballrt/tests/data/UniformCartesianGrid_E2E_mom0.nc and b/pinballrt/tests/data/UniformCartesianGrid_E2E_mom0.nc differ
diff --git a/pinballrt/tests/data/UniformCartesianGrid_E2E_scattering.npz b/pinballrt/tests/data/UniformCartesianGrid_E2E_scattering.npz
index 503e55f..2854a7d 100644
Binary files a/pinballrt/tests/data/UniformCartesianGrid_E2E_scattering.npz and b/pinballrt/tests/data/UniformCartesianGrid_E2E_scattering.npz differ
diff --git a/pinballrt/tests/data/UniformCartesianGrid_E2E_temperature.npz b/pinballrt/tests/data/UniformCartesianGrid_E2E_temperature.npz
index 7fa1900..7d729d6 100644
Binary files a/pinballrt/tests/data/UniformCartesianGrid_E2E_temperature.npz and b/pinballrt/tests/data/UniformCartesianGrid_E2E_temperature.npz differ
diff --git a/pinballrt/tests/data/UniformSphericalGrid_E2E_image.nc b/pinballrt/tests/data/UniformSphericalGrid_E2E_image.nc
index 6c8b54d..a2e4706 100644
Binary files a/pinballrt/tests/data/UniformSphericalGrid_E2E_image.nc and b/pinballrt/tests/data/UniformSphericalGrid_E2E_image.nc differ
diff --git a/pinballrt/tests/data/UniformSphericalGrid_E2E_mom0.nc b/pinballrt/tests/data/UniformSphericalGrid_E2E_mom0.nc
index 93a7356..3d0453f 100644
Binary files a/pinballrt/tests/data/UniformSphericalGrid_E2E_mom0.nc and b/pinballrt/tests/data/UniformSphericalGrid_E2E_mom0.nc differ
diff --git a/pinballrt/tests/data/UniformSphericalGrid_E2E_scattering.npz b/pinballrt/tests/data/UniformSphericalGrid_E2E_scattering.npz
index 3b34253..6a6e11b 100644
Binary files a/pinballrt/tests/data/UniformSphericalGrid_E2E_scattering.npz and b/pinballrt/tests/data/UniformSphericalGrid_E2E_scattering.npz differ
diff --git a/pinballrt/tests/data/UniformSphericalGrid_E2E_temperature.npz b/pinballrt/tests/data/UniformSphericalGrid_E2E_temperature.npz
index 61f613e..aae6817 100644
Binary files a/pinballrt/tests/data/UniformSphericalGrid_E2E_temperature.npz and b/pinballrt/tests/data/UniformSphericalGrid_E2E_temperature.npz differ
diff --git a/pinballrt/tests/data/diana.npz b/pinballrt/tests/data/diana.npz
new file mode 100644
index 0000000..c27a573
Binary files /dev/null and b/pinballrt/tests/data/diana.npz differ
diff --git a/pinballrt/tests/data/diana_wice.dst b/pinballrt/tests/data/diana_wice.dst
index 85d38d6..669e0c5 100644
Binary files a/pinballrt/tests/data/diana_wice.dst and b/pinballrt/tests/data/diana_wice.dst differ
diff --git a/pinballrt/tests/test_E2E.py b/pinballrt/tests/test_E2E.py
index dac1936..884bf80 100644
--- a/pinballrt/tests/test_E2E.py
+++ b/pinballrt/tests/test_E2E.py
@@ -64,7 +64,7 @@ def test_E2E(grid_class, grid_kwargs, percentile, return_vals=False):
     g.set_properties_from_lambda('co.dat')
 
     cube = model.make_image(npix=256, pixel_size=0.2*u.arcsec, channels=np.linspace(-20., 20., 300)*u.km/u.s, rest_frequency=g.nu[2], 
-                            incl=45.*u.degree, pa=45.*u.degree, distance=1.*u.pc, include_dust=False, device='cpu')
+                            incl=45.*u.degree, pa=45.*u.degree, distance=1.*u.pc, include_dust=False, device='cpu', include_sources=False)
     mom0 = cube.sum(dim='lam')
 
     # Do the checks.
diff --git a/pinballrt/tests/test_dust_creation.py b/pinballrt/tests/test_dust_creation.py
index afe1774..e8d0f7a 100644
--- a/pinballrt/tests/test_dust_creation.py
+++ b/pinballrt/tests/test_dust_creation.py
@@ -1,6 +1,7 @@
-from pinballrt.dust import Dust, load
+from pinballrt.dust import IsotropicDust, HenyeyGreensteinDust, GeneralDust, load, suggest_opacity_sampling
 import numpy as np
 import astropy.units as u
+import pytest
 import dill
 import os
 
@@ -9,62 +10,169 @@ def test_Dust():
     Test the Dust class by creating a dust file from input opacity data.
     """
 
-    data = np.load(os.path.join(os.path.dirname(__file__), "data/diana_wice.npz"))
+    data = np.load(os.path.join(os.path.dirname(__file__), "data/diana.npz"))
 
-    d = Dust(lam=data["lam"]*u.cm, kabs=data["kabs"]*u.cm**2/u.g, ksca=data["ksca"]*u.cm**2/u.g, amax=data["amax"]*u.cm, p=data["p"])
+    p, amax = np.meshgrid(data["p"], data["amax"], indexing="ij")
 
-    assert d.kmean.value == 2206.6072
+    d = IsotropicDust(lam=data["lam"]*u.cm, 
+                      kabs=data["kabs"]*u.cm**2/u.g, 
+                      ksca=data["ksca"]*u.cm**2/u.g, 
+                      amax=amax*u.cm, 
+                      p=p)
+
+    assert d.kmean.value == 4574.907442551958
 
     d.save("amax.dst")
 
-def test_learning():
+@pytest.mark.parametrize(
+    "dust_type,dims",
+    [
+        pytest.param(IsotropicDust, (), id="IsotropicDust/None"),
+        pytest.param(IsotropicDust, ("amax","abundances"), id="IsotropicDust/amax,abundances"),
+        pytest.param(IsotropicDust, ("p","amax","abundances"), id="IsotropicDust/p,amax,abundances"),
+        pytest.param(HenyeyGreensteinDust, (), id="HenyeyGreensteinDust/None"),
+        pytest.param(HenyeyGreensteinDust, ("p","abundances"), id="HenyeyGreensteinDust/p,abundances"),
+        pytest.param(HenyeyGreensteinDust, ("p","amax","abundances"), id="HenyeyGreensteinDust/p,amax,abundances"),
+        pytest.param(GeneralDust, (), id="GeneralDust/None"),
+        pytest.param(GeneralDust, ("p","abundances"), id="GeneralDust/p,abundances"),
+        pytest.param(GeneralDust, ("p","amax","abundances"), id="GeneralDust/p,amax,abundances"),
+    ]
+)
+def test_learning(dust_type, dims):
     """
     Test the learn_random_nu method of the Dust class.
     """
-
-    data = np.load(os.path.join(os.path.dirname(__file__), "data/diana_wice.npz"))
-
-    d = Dust(lam=data["lam"]*u.cm, kabs=data["kabs"]*u.cm**2/u.g, ksca=data["ksca"]*u.cm**2/u.g, amax=data["amax"]*u.cm, p=data["p"])
+    import matplotlib
+    matplotlib.use("Agg")
+    
+    wavelengths = np.logspace(-1, 4, 10) * u.micron
+
+    if "p" in dims:
+        p_range = (2.5, 4.5)
+    else:
+        p_range = None
+
+    if "amax" in dims:
+        amax_range = (1*u.micron, 10*u.cm)
+    else:
+        amax_range = None
+
+    if "abundances" in dims:
+        n_dust_subspecies = 3
+    else:
+        n_dust_subspecies = 1
+
+    samples = suggest_opacity_sampling(10 if len(dims) > 0 else 0, p_range=p_range, amax_range=amax_range, n_dust_subspecies=n_dust_subspecies)
+    samples = np.moveaxis(np.repeat(np.expand_dims(samples, 1), wavelengths.size, axis=1), -1, 0)
+
+    index = 0
+    if "p" in dims:
+        p = samples[index]
+        index += 1
+    else:
+        p = 3.5
+
+    if "amax" in dims:
+        amax = samples[index] * u.cm
+        index += 1
+    else:
+        amax = 1.0*u.micron
+
+    if "abundances" in dims:
+        silicate_fraction = samples[index]
+        index += 1
+        water_fraction = samples[index]
+    else:
+        silicate_fraction = 0.6
+        water_fraction = 0.3
+
+    wavelengths = np.repeat(np.expand_dims(wavelengths, axis=0), max(samples.shape[1], 1), axis=0)
+
+    # Define the absorption and scattering opacities (in cm^2/g).
+    silicate_feature = 100**silicate_fraction * np.exp(-0.5*(wavelengths - 10*u.micron)**2 / (10.*u.micron)**2) * u.cm**2 / u.g
+    water_feature = 100**water_fraction * np.exp(-0.5*(wavelengths - 3.*u.micron)**2 / (2.*u.micron)**2) * u.cm**2 / u.g
+
+    power_law_index = (-2. / (1 + np.exp(-p/3.5 * (-1.5 - np.log10(amax.to(u.cm).value)))))
+
+    kappa_abs = 1.0 * (wavelengths.to(u.micron).value/100.0)**power_law_index * u.cm**2 / u.g + silicate_feature + water_feature
+    kappa_scat = 0.5 * (wavelengths.to(u.micron).value/100.0)**power_law_index * u.cm**2 / u.g + silicate_feature + water_feature
+
+    # Create the Dust object.
+    dust_kwargs = {"lam":wavelengths[0,:], 
+                   "amax":amax[:,0] if "amax" in dims else None, 
+                   "p":p[:,0] if "p" in dims else None, 
+                   "abundances":(silicate_fraction[:,0], water_fraction[:,0]) if "abundances" in dims else (),
+                   "kabs":kappa_abs, 
+                   "ksca":kappa_scat,
+                   "ntemperatures":10}
+
+    if dust_type == HenyeyGreensteinDust:
+        g = np.tanh(p - np.log10(wavelengths.to(u.micron).value))
+        dust_kwargs["g"] = g
+    elif dust_type == GeneralDust:
+        g = np.repeat(np.expand_dims(np.tanh(p - np.log10(wavelengths.to(u.micron).value)), axis=-1), 5, axis=-1)
+        theta = np.tile(np.expand_dims(np.linspace(0, 180., 5), axis=(0,1)), (10 if len(dims) > 0 else 1, 10, 1)) * u.deg
+        scattering_phase_function = (1 - g**2) / (4 * np.pi * (1 + g**2 - 2*g*np.cos(theta.to(u.rad).value))**(3/2))
+
+        dust_kwargs["scattering_phase_function"] = scattering_phase_function
+        dust_kwargs["theta"] = theta[0,0,:]
+
+    d = dust_type(**dust_kwargs)
 
     # Test the learn_random_nu method.
 
-    n_samples = 1000
+    models = ["kabs","ksca","pmo","random_nu"]
+    if dust_type == HenyeyGreensteinDust:
+        models.append("g")
+    if dust_type == GeneralDust:
+        models.append("scattering_phase_function")
+        models.append("random_direction")
 
-    for model in ["kabs","ksca","pmo","random_nu"]:
+    for model in models:
         print('*****************************')
         print(f'{model}')
         print('*****************************')
-        d.learn(model=model, nsamples=n_samples, overwrite=True)
-        d.fit(epochs=10)
+        d.learn(model=model, overwrite=True)
+        if model == "random_nu":
+            size = d.kabs.size * d.temperature.size
+        elif model == "pmo":
+            size = d.kabs.shape[0] * d.temperature.size
+        else:
+            size = d.kabs.size
+        batch_size = int(10.**int(np.log10(size / 10)))
+        d.fit(epochs=10, batch_size=batch_size)
         d.test_model(plot=True)
 
-def test_learn_ml_step():
-    """
-    Test the learn_ml_step method of the Dust class.
-    """
-    
-    print('*****************************')
-    print('ml_step')
-    print('*****************************')
-    
-    # Load the dust file.
-
-    d = load(os.path.join(os.path.dirname(__file__), "data/diana_wice.dst"))
+        if model == "random_nu":
+            d.plot_random_nu_model(100)
 
     # Test the learn_ml_step method.
 
     n_samples = 100
-    d.learn(model="ml_step", hidden_units=((32, 32, 32),)*6, nsamples=n_samples, tau_range=(0.5, 1.0), nu_range=(d.nu.max()/10, d.nu.max()))
+    d.learn(model="ml_step", 
+            hidden_units=((32, 32, 32),)*6, 
+            nsamples=n_samples, 
+            tau_range=(0.5, 1.0), 
+            nu_range=(d.nu.max()/10, d.nu.max()), 
+            overwrite=True)
     d.fit(epochs=10)
     d.test_model(plot=True)
 
+    os.system("rm -rf sim_results.csv *_logs")
+
 def test_dust_pickle():
     """
     Test that Dust classes can be pickled and unpickled.
     """
 
-    data = np.load(os.path.join(os.path.dirname(__file__), "data/diana_wice.npz"))
+    data = np.load(os.path.join(os.path.dirname(__file__), "data/diana.npz"))
+
+    p, amax = np.meshgrid(data["p"], data["amax"], indexing="ij")
 
-    d = Dust(lam=data["lam"]*u.cm, kabs=data["kabs"]*u.cm**2/u.g, ksca=data["ksca"]*u.cm**2/u.g, amax=data["amax"]*u.cm, p=data["p"])
+    d = IsotropicDust(lam=data["lam"]*u.cm, 
+                      kabs=data["kabs"]*u.cm**2/u.g, 
+                      ksca=data["ksca"]*u.cm**2/u.g, 
+                      amax=amax*u.cm, 
+                      p=p)
 
     result = dill.loads(dill.dumps(d))
diff --git a/pinballrt/tests/test_grids.py b/pinballrt/tests/test_grids.py
index 612346d..0f4f8a1 100644
--- a/pinballrt/tests/test_grids.py
+++ b/pinballrt/tests/test_grids.py
@@ -50,18 +50,22 @@ def test_grid_physical_properties_shapes():
     Test that the grid shapes are correct.
     """
 
+    # Set up the dust.
+
+    d = os.path.join(os.path.dirname(__file__), "data/diana_wice.dst")
+
     # Set up the grid.
     model = Model(grid=UniformCartesianGrid, grid_kwargs={"ncells":9, "dx":2.0*u.au})
 
     density = np.ones(model.grid.shape)*1.0e-16 * u.g / u.cm**3
     
-    model.set_physical_properties(density=density, amax=100*u.micron, p=3.5, dust="diana_wice.dst")
+    model.set_physical_properties(density=density, amax=100*u.micron, p=3.5, dust=d)
     
     assert model.grid.grid.dust_density.numpy().shape == model.grid.shape
     assert model.grid.grid.amax.numpy().shape == model.grid.shape
     assert model.grid.grid.p.numpy().shape == model.grid.shape
 
-    model.set_physical_properties(density=density, amax=100, p=3.5, dust="diana_wice.dst")
+    model.set_physical_properties(density=density, amax=100, p=3.5, dust=d)
 
     assert model.grid.grid.dust_density.numpy().shape == model.grid.shape
     assert model.grid.grid.amax.numpy().shape == model.grid.shape
diff --git a/pinballrt/utils.py b/pinballrt/utils.py
index 57890dd..fb048f8 100644
--- a/pinballrt/utils.py
+++ b/pinballrt/utils.py
@@ -69,6 +69,23 @@ def calculate_Qvalue(array1, array2, percentile=99.0, clip=None):
     
     return Q
 
+@wp.kernel
+def random_direction(direction: wp.array(dtype=wp.vec3),
+                     iphotons: wp.array(dtype=int),
+                     seed: int): # pragma: no cover
+    i = wp.tid()
+    ip = iphotons[i]
+
+    rng = wp.rand_init(seed, i)
+
+    cost = -1. + 2.*wp.randf(rng)
+    sint = wp.sqrt(1.-cost**2.)
+    phi = 2.*np.pi*wp.randf(rng)
+
+    direction[ip][0] = sint*np.cos(phi)
+    direction[ip][1] = sint*np.sin(phi)
+    direction[ip][2] = cost
+
 @wp.struct
 class GridStruct:
     w1: wp.array(dtype=float)
@@ -96,9 +113,11 @@ class GridStruct:
     energy: wp.array3d(dtype=float)
     amax: wp.array3d(dtype=float)
     p: wp.array3d(dtype=float)
+    dust_abundances: wp.array4d(dtype=float)
 
     kabs: wp.array4d(dtype=float)
     ksca: wp.array4d(dtype=float)
+    g: wp.array4d(dtype=float)
 
     velocity: wp.array4d(dtype=float)
     microturbulence: wp.array3d(dtype=float)