DeepH-pack — Graph Neural Network for DFT Hamiltonians
DeepH-pack is a graph neural network framework for representing density functional theory Hamiltonians. As a core developer, I have contributed to:
- Interfaces with multiple DFT codes: SIESTA, FHI-aims, ABACUS, pyscf, and GPAW
- Post-processing tools for DeepH calculations