materialsframework is a modular Python framework specifically built for deploying, benchmarking, and experimenting with machine learning interatomic potentials (MLIPs). It provides researchers with tools to test and compare different ML models in the context of materials modeling and simulation. The project is open source under the GPL-3.0 license.
In the Data science & ML workbench space, materialsframework takes a focused approach. Efficiently deploying, benchmarking, and experimenting with machine learning interatomic potentials in materials science research. materialsframework is an open-source project aimed at materials scientists. The project is open source (GPL-3.0). It runs on the command line, and it can be self-hosted.
dogusariturk builds and maintains materialsframework, and it first shipped in 2024. The project is developed in the open on GitHub with 27 stars and 163 commits in the last 90 days. Key capabilities include MLIP benchmarking, modular architecture, and experiment management.
Summary written by a language model from the project’s public pages.
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