MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials Through an Open and Accessible Benchmark Platform
Abstract
Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an overreliance on error-based metrics tied to specific density functional theory (DFT) references. We introduce MLIP Arena, a benchmark platform that evaluates MLIPs based on physics awareness, chemical reactivity, stability under extreme conditions, and predictive capabilities for thermodynamic properties and physical phenomena. Our evaluation challenges previous assumptions about model architectures and performance. MLIP Arena provides a reproducible framework to guide MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency. The Python package and online leaderboard are available at https://huggingface.co/spaces/atomind/mlip-arena
Cite
Text
Chiang et al. "MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials Through an Open and Accessible Benchmark Platform." ICLR 2025 Workshops: AI4MAT, 2025.Markdown
[Chiang et al. "MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials Through an Open and Accessible Benchmark Platform." ICLR 2025 Workshops: AI4MAT, 2025.](https://mlanthology.org/iclrw/2025/chiang2025iclrw-mlip/)BibTeX
@inproceedings{chiang2025iclrw-mlip,
title = {{MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials Through an Open and Accessible Benchmark Platform}},
author = {Chiang, Yuan and Kreiman, Tobias and Weaver, Elizabeth and Amin, Ishan and Kuner, Matthew and Zhang, Christine and Kaplan, Aaron and Chrzan, Daryl and Blau, Samuel M and Krishnapriyan, Aditi S. and Asta, Mark},
booktitle = {ICLR 2025 Workshops: AI4MAT},
year = {2025},
url = {https://mlanthology.org/iclrw/2025/chiang2025iclrw-mlip/}
}