Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
Christian M. Clausen Jan Rossmeisl Zachary W. Ulissi
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.