kYangLi/DeepH-pack-docs
The documentation of DeepH-pack, the latest iteration of DeepH, unites all the preceding DeepH methodologies into a cohesive package. This advanced version has been meticulously rewritten with JAX, enhancing its efficiency and capabilities.
DeepH-pack helps scientists perform electronic structure calculations more efficiently and accurately. It takes in material structure data and, using deep learning, predicts electronic properties and behaviors. This tool is for computational materials scientists, chemists, and physicists who need to simulate material properties quickly without sacrificing accuracy.
Use this if you need to accelerate ab initio electronic structure calculations for materials science, chemistry, or physics research using a neural network approach.
Not ideal if you prefer traditional Density Functional Theory (DFT) methods without machine learning integration, or if you don't have access to GPU-accelerated computing.
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Language
Python
License
AGPL-3.0
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Last pushed
Feb 27, 2026
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