DeePTB Documentation#
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:
DeePTB-SK: deep learning based local environment dependent Slater-Koster TB.
Customizable Slater-Koster parameterization with neural network corrections.
Flexible basis and exchange-correlation functional choices.
Handle systems with strong spin-orbit coupling (SOC) effects.
DeePTB-E3: E3-equivariant neural networks for representing quantum operators.
Construct DFT Hamiltonians/density and overlap matrices under full LCAO basis.
Utilize (Strictly) Localized Equivariant Message-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:
INPUT TAG
Advanced
Citing DeePTB
Developing Team