atomistic-machine-learning/schnetpack
SchNetPack - Deep Neural Networks for Atomistic Systems
This tool helps scientists and engineers working with molecules and materials to predict their quantum-chemical properties, like potential energy surfaces, dipole moments, and forces. You provide atomic structure data, and the tool outputs predictions of these properties, which are crucial for understanding material behavior and designing new compounds. It's designed for researchers and computational chemists who need to model complex atomic interactions.
912 stars. Actively maintained with 1 commit in the last 30 days.
Use this if you need to accurately predict quantum-chemical properties of molecules and materials using deep learning, especially for tasks like molecular dynamics simulations.
Not ideal if you are looking for a user-friendly GUI-based simulation tool or do not have experience with command-line interfaces and scientific computing.
Stars
912
Forks
251
Language
Python
License
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Category
Last pushed
Mar 12, 2026
Commits (30d)
1
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