An evaluation framework for machine learning models simulating high-throughput materials discovery.
machine-learning convex-hull bayesian-optimization materials-discovery interatomic-potential high-throughput-search
- Updated
Dec 1, 2025 - Python
#
interatomic-potential
Here are 3 public repositories matching this topic...
GUI for running simulations with universal machine learning interatomic potentials (MACE, CHGNet, SevenNet, Nequix, ORB, MatterSim, PET-MAD))
genetic-algorithm molecular-dynamics ml phonons mace elastic-properties geometry-optimization streamlit point-defects bulk-modulus machine-learning-potential interatomic-potential mlip elastic-constants chgnet sevennet nequix mattersim
- Updated
Dec 3, 2025 - Python
The Orchestrator is an integrated software package for building, training, testing, augmenting, running, and analyzing interatomic potentials (IAPs) and their simulations.
python application machine-learning materials chemistry simulation hpc molecular-dynamics data-analysis data-management materials-science workflows uncertainty-quantification interatomic-potential
- Updated
Oct 9, 2025 - Python
Improve this page
Add a description, image, and links to the interatomic-potential topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the interatomic-potential topic, visit your repo's landing page and select "manage topics."