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2 changes: 1 addition & 1 deletion README.md
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# About ABACUS

ABACUS (Atomic-orbital Based Ab-initio Computation at UStc) is an open-source package based on density functional theory (DFT). The package utilizes both plane wave and numerical atomic basis sets with the usage of norm-conserving pseudopotentials to describe the interactions between nuclear ions and valence electrons. ABACUS supports LDA, GGA, meta-GGA, and hybrid functionals. Apart from single-point calculations, the package allows geometry optimizations and ab-initio molecular dynamics with various ensembles. The package also provides a variety of advanced functionalities for simulating materials, including the DFT+U, VdW corrections, and implicit solvation model, etc. In addition, ABACUS strives to provide a general infrastructure to facilitate the developments and applications of novel machine-learning-assisted DFT methods (DeePKS, DP-GEN, DeepH, etc.) in molecular and material simulations.
ABACUS (Atomic-orbital Based Ab-initio Computation at UStc) is an open-source package based on density functional theory (DFT). The package utilizes both plane wave and numerical atomic basis sets with the usage of norm-conserving pseudopotentials to describe the interactions between nuclear ions and valence electrons. ABACUS supports LDA, GGA, meta-GGA, and hybrid functionals. Apart from single-point calculations, the package allows geometry optimizations and ab-initio molecular dynamics with various ensembles. The package also provides a variety of advanced functionalities for simulating materials, including the DFT+U, VdW corrections, and implicit solvation model, etc. In addition, ABACUS strives to provide a general infrastructure to facilitate the developments and applications of novel machine-learning-assisted DFT methods (DeePKS, DP-GEN, DeepH, DeePTB etc.) in molecular and material simulations.

# Online Documentation
For detailed documentation, please refer to [our documentation website](https://abacus.deepmodeling.com/).
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# DeePTB

[DeePTB](https://github.com/deepmodeling/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. See more details in [DeePTB-SK: Nat Commun 15, 6772 (2024)](https://www.nature.com/articles/s41467-024-51006-4) and [DeePTB-E3: arXiv:2407.06053](https://arxiv.org/pdf/2407.06053).

DeePTB trains the model based on the Structure, Eigenvalues, Hamiltonian, Density matrix, and Overlap matrix from first-principles calcualtions. DeePTB team provides the interfaces [dftio](https://github.com/deepmodeling/dftio) with other first-principles softwares. [dftio](https://github.com/deepmodeling/dftio) fully supports the interfaces with ABACUS, and can transfer the Structure, Eigenvalues, Hamiltonian, Density matrix, and Overlap matrix from ABACUS into the format used in [DeePTB](https://github.com/deepmodeling/DeePTB).
1 change: 1 addition & 0 deletions docs/advanced/interface/index.rst
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deepks
dpgen
deeph
deeptb
Hefei-NAMD
phonopy
Wannier90
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