Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials

arXiv:2403.09811v1 Announce Type: cross
Abstract: Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates. They present bottlenecks to the conventional use of density functional theory calculations, and consequently, the use of machine-learned potentials is becoming increasingly prevalent in atomic structure simulations. In this communication, we show the results of adjusting and fine-tuning the pretrained EquiformerV2 model from the Open Catalyst Project to infer adsorption energies of *OH and …

abstract arxiv bottlenecks computational cond-mat.mtrl-sci cs.lg entropy functional machine materials physics.chem-ph research simulations spaces studies theory type