kYangLi/DeepH-dock
DeepH-dock seamlessly integrates deep learning with first-principles calculations. It serves as a modular and extensible bridge, functioning both as the dedicated interface for the DeepH-pack suite and as a standalone tool for coupling deep learning models with computational materials science workflows.
This tool helps computational materials scientists seamlessly connect their existing first-principles calculations, like those from DFT software, with deep learning models, specifically the DeepH method. It takes raw data from various quantum materials simulations and produces standardized data formats and deep learning-based Hamiltonians. Researchers and engineers in materials science can use this to integrate advanced deep learning into their materials design and analysis workflows.
Use this if you need to integrate deep learning models, especially the DeepH method, directly into your quantum materials calculation workflows and manage data from various DFT software packages.
Not ideal if you are not working with first-principles calculations or specifically the DeepH method for materials science.
Stars
29
Forks
2
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/kYangLi/DeepH-dock"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pritampanda15/PandaDock
PandaDock: Physics based Molecular Docking with GNN Scoring
kexinhuang12345/DeepPurpose
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
BioinfoMachineLearning/PoseBench
Comprehensive benchmarking of protein-ligand structure prediction methods. (Nature Machine Intelligence)
maranasgroup/CatPred
Machine Learning models for in vitro enzyme kinetic parameter prediction
kamerlinlab/KIF
KIF - Key Interactions Finder. A python package to identify the key molecular interactions that...