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...