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DeepH-pack is the official implementation of the DeepH (Deep Hamiltonian) method described in the paper Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation and in the Research Briefing. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA and will support HONPAS.
Features
- Deep neural networks for density functional theory Hamiltonian
- Prepare the Julia 1.6.6 interpreter
- One of the supported DFT packages is required to obtain the dataset and calculate the overlap matrix for large-scale material systems
- Train is a part of DeepH-pack
- Inference is a part of DeepH-pack
- Documentation available
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Data VisualizationLicense
GNU Library or Lesser General Public License version 3.0 (LGPLv3)Follow DeepH-pack
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