DeepH-pack’s documentation

DeepH-pack is a package for the application of deep neural networks to the prediction of density functional theory (DFT) Hamiltonian matrices based on local coordinates and basis transformation 1. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA, and will support HONPAS soon.



H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, Y. Xu. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. Nat. Comput. Sci. 2, 367–377 (2022).