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  • Independent Researcher | Open to industry roles
  • Bengaluru
  • LinkedIn in/anjali-rameja

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anjali-shankar/README.md

Hi, I'm Anjali

I'm a computational physicist (PhD) building physics-aware machine-learning surrogates for atomistic simulation. I handle the whole loop: DFT data, model training, validation against physics and experiment, and deployment on multi-node GPUs.

🌐 anjali-shankar.github.io · 📄 CV (PDF)

Now

Fine-tuning the MACE-MPA-0 atomistic foundation model for Mg-Ca metallic glasses (independent researcher, 2026 to present)

  • 2,000-geometry DFT training set (Quantum ESPRESSO) across crystal, liquid, supercooled and glassy states, with an active-learning round targeting the model's errors
  • LAMMPS + MACE (libtorch, Kokkos, CUDA) on multi-node A100 GPUs: 96% weak-scaling efficiency on 8 nodes, up to 3.3x throughput from memory tuning
  • Zero-shot MACE reproduces the glass's Mg-to-Ca icosahedral-centre ratio (3.3, against 5.6 from experiment), where classical 2NN-MEAM gives 1.0
  • Completion expected Nov 2026, with a public release of the fine-tuned MLIP and a journal paper to follow
  • Co-Investigator on a PARAM Rudra (National Supercomputing Mission) A100 allocation

I also write and review scientific-coding and physics evaluation tasks for frontier AI labs (Turing, Aligned, Handshake AI).

Background

  • Six years of molecular dynamics and DFT on bulk metallic glasses (Mg₂Ca, Al₂Ca, Al₄Ca, Ca₆₁Al₃₉) and liquid W and Mo, from 300 K to 5800 K
  • Transport (Green-Kubo, Stokes-Einstein breakdown), glass transition, elastic properties and structure (RDF, S(q), CNA)
  • Python analysis pipelines: metallic-glass-md

Selected publications (as A. Shankar)

  • J. Phys.: Condens. Matter 37, 355401 (2025). Atomic mass transport in binary metallic glasses.
  • J. Appl. Phys. 136, 035107 (2024), Featured Article. Mg-Ca/Al-Ca BMGs vs crystalline alloys for bone grafting.
  • Physica Scripta 98, 115963 (2023). Mass transport and thermal properties of liquid tungsten.

All six papers on Google Scholar

Open to

Computational materials scientist and scientific ML roles in Bengaluru: biomedical, semiconductor, energy storage, structural alloys and R&D centres.

Find me: Website · LinkedIn · Google Scholar · anjalirameja1993@gmail.com

Popular repositories Loading

  1. anjali-shankar anjali-shankar Public

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  2. metallic-glass-md metallic-glass-md Public

    LAMMPS inputs and Python analysis for Mg-Ca and Al-Ca bulk metallic glasses: structure factors, RDFs, and diffusion from MD

    Python

  3. anjali-shankar.github.io anjali-shankar.github.io Public

    Personal site of Anjali S Rameja, computational physicist

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