https://github.com/alifayyazmalik/tfm-chemistry

Supporting code, mock data, and HPC-optimized PDE solvers for 'Time as the Architect of Atoms' (TFM Series #21). Includes synthetic data generation for wave-lump coherence effects in atomic spectra, bonding, and reaction kinetics. Built with Python/FEniCS and scalable to 500+ atoms via wavelet-based adaptive meshes

https://github.com/alifayyazmalik/tfm-chemistry

Repository

Supporting code, mock data, and HPC-optimized PDE solvers for 'Time as the Architect of Atoms' (TFM Series #21). Includes synthetic data generation for wave-lump coherence effects in atomic spectra, bonding, and reaction kinetics. Built with Python/FEniCS and scalable to 500+ atoms via wavelet-based adaptive meshes

https://github.com/alifayyazmalik/TFM-Chemistry/blob/main/

# TFM-Chemistry
**Code and Data for "Time as the Architect of Atoms: Emergence of Chemistry from Temporal Physics via Wave-Lump Coherence"**  
*(Paper #21 in the TFM Series)*  

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.XXXXXXX.svg)](https://doi.org/10.XXXX/zenodo.XXXXXXX)  
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)  
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alifayyaz/TFM-Chemistry/HEAD)  

Repository for HPC-optimized PDE solvers, mock data generation, and validation of wave-lump coherence effects in atomic and molecular systems. Supports Figures 15 of the paper.
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# Ethics & Transparency  
For details on synthetic data generation and reproducibility, see [ETHICS.md](ETHICS.md).  
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## Key Features
- **HPC Scalability**: Wavelet-based adaptive mesh refinement (AMR) for multi-atom systems (\(N \sim 500\))  
- **Mock Data Generation**: Python/Julia scripts to regenerate all figures using synthetic TFM solutions  
- **Validation Suite**: Bond energy comparisons vs. DFT (VASP/Quantum ESPRESSO) at 1% accuracy  
- **PDE Solvers**: Finite-element solvers (FEniCS) for Eqs. 4, 7, 1214 of the paper  

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## Validate TFM Predictions  
1. **Run real HPC simulations**:  
   - Use parameters from [`src/params/constants.py`](src/params/constants.py)  
   - Start with [`templates/hpc_slurm.sh`](templates/hpc_slurm.sh)  
2. **Compare to experiments**:  
   - Test Rydberg state deviations (Section 5.1 of [paper](LINK))  
   - Measure oscillatory reaction rates (Section 3.2)  
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## Installation

### Dependencies
- Python 3.8+  
- MPI (for HPC parallelization)  
- FEniCS 2019+ (PDE backend)  
- Julia 1.6+ (optional for wavelet AMR)  

### Quick Setup (Local)
```bash
# Clone repository
git clone https://github.com/alifayyaz/TFM-Chemistry.git
cd TFM-Chemistry

# Install Python requirements
pip install -r requirements.txt  # numpy, scipy, fenics, matplotlib

# Optional: Create Conda environment
conda env create -f environment.yml
conda activate tfm-chemistry

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