A Python project that applies AIFeynman to discover physical laws in condensed matter systems. This project demonstrates how machine learning can be used to identify fundamental relationships in complex quantum systems.
This project uses AIFeynman to:
- Discover physical laws from experimental data in condensed matter systems
- Analyze quantum phase transitions
- Model electronic properties of materials
- Predict material properties based on fundamental parameters
- Data generation for various condensed matter systems
- AIFeynman integration for symbolic regression
- Visualization tools for physical laws discovery
- Benchmarking against known theoretical models
# Clone the repository
git clone https://github.com/BekturMur/AIFeynman.git
cd CondensedMatterAI
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtfrom condensed_matter_ai import MaterialAnalyzer
# Initialize analyzer
analyzer = MaterialAnalyzer()
# Load experimental data
data = analyzer.load_data("path_to_data.csv")
# Discover physical laws
laws = analyzer.discover_laws(data)
# Visualize results
analyzer.visualize_results(laws)CondensedMatterAI/
├── src/
│ ├── __init__.py
│ ├── material_analyzer.py
│ ├── data_generator.py
│ └── visualization.py
├── tests/
│ ├── __init__.py
│ └── test_material_analyzer.py
├── examples/
│ └── example_usage.py
├── requirements.txt
└── README.md
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
If you use this project in your research, please cite:
@software{CondensedMatterAI,
author = {Your Name},
title = {CondensedMatterAI: AIFeynman for Condensed Matter Physics},
year = {2024},
publisher = {GitHub},
url = {https://github.com/BekturMur/AIFeynman}
}