HQDA: Hybrid-Quantum Dynamics Analysis Framework

Zero-Base Reconstruction of PbTiO₃ Phase Transitions

HQDA is a high-precision computational framework designed for the rigorous analysis of Ferroelectric Phase Transitions in perovskite oxides, specifically Lead Titanate (PbTiO₃). By integrating Density Functional Theory (DFT) baselines with Ab Initio Molecular Dynamics (AIMD) and Machine Learning potentials (DeepMD), HQDA provides a "Zero-Base" audit trail that eliminates hard-coded empirical parameters.

🚀 Key Features

• Zero-Base Constants Audit: Automatically extracts and verifies fundamental physical constants (Total Energy, Lattice Parameters) directly from raw VASP OSZICAR and CONTCAR files.
• Finite-Size Scaling (FSS): Implements rigorous statistical mechanics scaling laws to extrapolate macroscopic transition temperatures ($T_c$) from finite supercell simulations.
• Judge-Witness Engine: A unique "Synthesis" module that acts as an impartial adjudicator, comparing theoretical predictions against experimental "witness" data.
• DeepMD Integration: Seamlessly handles data from Deep Potential Molecular Dynamics for extended time-scale simulations.
• Automated Manuscript Generation: Capable of synthesizing results into structured scientific reports.

🛠️ Installation

git clone https://github.com/YourUsername/HQDA.git
cd HQDA
pip install -r requirements.txt

💻 Usage

The core pipeline is orchestrated by run_hqda.py.

1. Full Analysis Pipeline

Runs the complete audit, scaling analysis, synthesis, and validation:

python run_hqda.py

2. Audit Only

To strictly verify the underlying physical constants against VASP raw data:

python run_hqda.py --audit-only

3. Synthesis Report

To generate the "Judge-Witness" scientific narrative based on current data:

python run_hqda.py --synthesis-only

📂 Project Structure

• engine/: Core physics engines (Thermodynamics, Finite-Size Scaling, Constants).
• synthesis/: Logic for the "Judge-Witness" narrative generation.
• validation/: Experimental validation modules.
• output/: Generated reports, plots, and JSON summaries.

🔬 Scientific Context

This framework addresses the "Order-Disorder vs. Displacive" debate in ferroelectric transitions. By quantifying entropy contributions ($\Delta S = \Delta S_{vib} + \Delta S_{config}$) and applying Luttinger's Theorem constraints, HQDA offers a reconciled view of the cubic-to-tetragonal phase transition in $PbTiO_3$.

Keywords: Density Functional Theory (DFT), Molecular Dynamics, Phase Transitions, Ferroelectricity, PbTiO3, Machine Learning Potentials, DeepMD, VASP, Computational Materials Science.

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.