Universal machine-learning models for advanced atomistic simulations
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Jul 6, 2026 - Python
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machine-learned-interatomic-potentials
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Code for automated fitting of machine learned interatomic potentials.
atomistic-simulations high-throughput-computing atomistic-machine-learning atomistic-modelling machine-learned-interatomic-potentials ml-potentials mlips
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Jul 6, 2026 - Python
This repository investigates how different atomic descriptors (SOAP, Behler-Parrinello, Bispectrum, ChIMES, and Euler characteristic) induce sampling biases when curating MLIP training sets via Farthest Point Sampling, and whether the resulting latent spaces encode physically meaningful structure.
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Jul 1, 2026 - Python
Codes and data for the Review: "Machine-Learned Potentials for Solvation Modeling"
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Dec 30, 2025 - Jupyter Notebook
Files to reproduce results of a study on solvent-inclusive ML/MM simulations
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Dec 19, 2025 - Python
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