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Open access Sep 2026

Critical benchmarking of machine-learned interatomic potentials for intermolecular and noncovalent interactions

Accurate benchmarking of intermolecular interaction energies is central to evaluating quantum chemical methods and guiding the development of reliable machine-learned interatomic potentials (MLIPs). We benchmark five MLIPs (AIMNet2(2023), AIMNet2(2025), MACE-OFF23(M), MACE-OMol, and UMA-S-OMol) across twenty-one data...

K. Nayal, Ilkwon Cho, O. Isayev · 0 citations

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