DeePTB is a Python package that adopts the deep learning method to construct electronic tight-binding (TB) Hamiltonians using a minimal basis. Trained on smaller structures, DeePTB can efficiently predict TB Hamiltonians for large-size unseen structures. This feature enables efficient simulations of large-size systems under structural perturbations. Furthermore, DeePTB offers the ability to perform efficient and accurate finite temperature simulations, incorporating both atomic and electronic behavior through the integration of molecular dynamics (MD). Another significant advantage is that DeePTB is independent of the choice of various bases (PW or LCAO) and the exchange-correlation (XC) functionals (LDA, GGA, and even HSE) used in preparing the training labels. In addition, DeePTB can handle systems with strong spin-orbit coupling (SOC) effects. These capabilities make DeePTB adaptable to various research scenarios, extending its applicability to a wide range of materials and phenomena and offering a powerful and versatile tool for accurate and efficient simulations.
See more details in our DeePTB paper: arXiv:2307.04638
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.
For detailed documentation, please refer to our documentation website.