A new high-temperature AIMD benchmark for nine MOFs shows that top uMLIPs like ORB-v3 and fairchem OMAT still produce substantial errors in long-timescale dynamics despite lower static losses.
Machine- learned potentials for next-generation matter simulations.Nature Materials, 20(6):750–761, May 2021
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Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
A new high-temperature AIMD benchmark for nine MOFs shows that top uMLIPs like ORB-v3 and fairchem OMAT still produce substantial errors in long-timescale dynamics despite lower static losses.