atomind-ai/mlip-arena
🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://openreview.net/forum?id=SAT0KPA5UO
This platform helps materials scientists and computational chemists fairly evaluate different machine learning interatomic potentials (MLIPs). It takes various MLIP models as input and produces insights into their physical soundness and real-world performance, going beyond simple error metrics. The main users are researchers and developers who need to select or improve MLIPs for molecular and materials modeling.
Available on PyPI.
Use this if you need to rigorously benchmark and understand the strengths and weaknesses of different MLIPs for tasks like molecular dynamics simulations, especially when comparing models trained on diverse datasets.
Not ideal if you are looking for a simple, quick comparison based solely on basic error statistics, or if you primarily work with traditional DFT methods without incorporating MLIPs.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Feb 23, 2026
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0
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12
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