mlacs-developers/mlacs
A python library for machine-Learning assisted canonical sampling
This package helps materials scientists and computational chemists accurately simulate atomic systems at specific temperatures to understand their thermodynamic properties. You provide initial atomic configurations and quantum simulation parameters, and it outputs detailed thermodynamic properties like free energy, alongside trained machine learning potentials. Researchers in materials science, chemistry, and physics who work with atomic simulations will find this valuable.
Use this if you need to perform machine-learning assisted simulations of atomic systems to study their thermodynamic behavior and want to integrate advanced sampling techniques with quantum simulation codes.
Not ideal if you are looking for a simple, out-of-the-box solution for basic molecular dynamics without needing machine learning potentials or advanced free energy calculations.
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Language
Python
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Last pushed
Mar 04, 2026
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