https://github.com/deepmodeling/deeptb

DeePTB: A deep learning package for tight-binding approach with ab initio accuracy.
https://github.com/deepmodeling/deeptb

dft hamiltonian machine-learning slater-koster tight-binding

Last synced: 4 months ago
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DeePTB: A deep learning package for tight-binding approach with ab initio accuracy.

• Host: GitHub
• URL: https://github.com/deepmodeling/deeptb
• Owner: deepmodeling
• License: lgpl-3.0
• Created: 2023-07-11T03:19:42.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-13T08:10:52.000Z (about 1 year ago)
• Last Synced: 2024-05-14T00:06:28.549Z (about 1 year ago)
• Topics: dft, hamiltonian, machine-learning, slater-koster, tight-binding
• Language: Python
• Homepage:
• Size: 99.8 MB
• Stars: 31
• Watchers: 4
• Forks: 12
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/CONTRIBUTING.md
  • License: LICENSE

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README

        


    





 

 

 

 

 



## 🚀 About DeePTB

DeePTB is an innovative Python package that uses deep learning to accelerate *ab initio* electronic structure simulations. It offers versatile, accurate, and efficient simulations for a wide range of materials and phenomena. Trained on small systems, DeePTB can predict electronic structures of large systems, handle structural perturbations, and integrate with molecular dynamics for finite temperature simulations, providing comprehensive insights into atomic and electronic behavior.

- **Key Features**

DeePTB contains two main components: 

  1. **DeePTB-SK**: deep learning based local environment dependent Slater-Koster TB.

      - Customizable Slater-Koster parameterization with neural network corrections for . 

      - Flexible basis and exchange-correlation functional choices.

      - Handle systems with strong spin-orbit coupling (SOC) effects.

  2. **DeePTB-E3**: E3-equivariant neural networks for representing quantum operators.

      - Construct DFT Hamiltonians/density and overlap matrices under full LCAO basis.

      - Utilize (**S**trictly) **L**ocalized **E**quivariant **M**essage-passing (**(S)LEM**) model for high data-efficiency and accuracy.

      - Employs SO(2) convolution for efficient handling of higher-order orbitals in LCAO basis.

For more details, see our papers:

- [DeePTB-SK: Nat Commun 15, 6772 (2024)](https://doi.org/10.1038/s41467-024-51006-4)

- [DeePTB-E3: arXiv:2407.06053](https://arxiv.org/pdf/2407.06053)

## 🛠️ Installation

Installing **DeePTB** is straightforward. We recommend using a virtual environment for dependency management.

- **Requirements**

  - Python 3.8 or later.

  - Torch 1.13.0 or later ([PyTorch Installation](https://pytorch.org/get-started/locally)).

  - ifermi (optional, for 3D fermi-surface plotting).

- **Easy Installation**

  1. Using PyPi

  2. Ensure you have Python 3.8 or later and Torch installed.

  3. Install DeePTB with pip:

        ```bash

        pip install dptb

        ```

- **From Source**

    1. Clone the repository:

        ```bash

        git clone https://github.com/deepmodeling/DeePTB.git

        ```

    2. Navigate to the root directory and install DeePTB:

        ```bash

        cd DeePTB

        pip install .

        ```

## 📚 Documentation

- **Online documentation**

  

    For a comprehensive guide and usage tutorials, visit [our documentation website](https://deeptb.readthedocs.io/en/latest/).

- **Community**

    DeePTB joins the DeepModeling community, a community devoted of AI for science, as an incubating level project. To learn more about the DeepModeling community, see the [introduction of community](https://github.com/deepmodeling/community).

- **Contributing**

    We welcome contributions to DeePTB. Please refer to our [contributing guidelines](https://deeptb.readthedocs.io/en/latest/community/contribution_guide.html) for details.

## 🤝 How to Cite

The following references are required to be cited when using DeePTB. Specifically:

- **For DeePTB-SK:**

    Q. Gu, Z. Zhouyin, S. K. Pandey, P. Zhang, L. Zhang, and W. E, Deep Learning Tight-Binding Approach for Large-Scale Electronic Simulations at Finite Temperatures with Ab Initio Accuracy, Nat Commun 15, 6772 (2024).

  

- **For DeePTB-E3:**

  

    Z. Zhouyin, Z. Gan, S. K. Pandey, L. Zhang, and Q. Gu, Learning Local Equivariant Representations for Quantum Operators, arXiv:2407.06053.