most citedTowards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

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physics.chem-ph2025

MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform

Yuan Chiang, Tobias Kreiman, Christine Zhang +11

Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and…

physics.chem-ph2025★ 9 cited

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Ishan Amin, Sanjeev Raja, Aditi Krishnapriyan

The foundation model (FM) paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations and scalable training to perform a variety of c…