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 learning potentials for metal-organic frame- works using an incremental learning approach.npj Computational Materials, 9(1), February 2023
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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.