Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials

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 *O on the out-of-domain high-entropy alloy Ag–Ir–Pd–Pt–Ru. By applying an energy filter based on the local environment of the binding site, the zero-shot inference is markedly improved, and through few-shot fine-tuning the model yields state-of-the-art accuracy. It is also found that EquiformerV2, assuming the role of general machine learning potential, is able to inform a smaller, more focused direct inference model. This knowledge distillation setup boosts performance on complex binding sites. Collectively, this shows that foundational knowledge learned from ordered intermetallic structures can be extrapolated to the highly disordered structures of solid-solutions. With the vastly accelerated computational throughput of these models, hitherto infeasible research in the high-entropy material space is now readily accessible.