SurFF: a foundation model for surface exposure and morphology across intermetallic crystals

Abstract

With approximately 90% of industrial reactions occurring on surfaces, the role of heterogeneous catalysts is paramount. Currently, accurate surface exposure prediction is vital for heterogeneous catalyst design, but it is hindered by the high costs of experimental and computational methods. Here we introduce a foundation force-field-based model for predicting surface exposure and synthesizability (SurFF) across intermetallic crystals, which are essential materials for heterogeneous catalysts. We created a comprehensive intermetallic surface database using an active learning method and high-throughput density functional theory calculations, encompassing 12,553 unique surfaces and 344,200 single points. SurFF achieves density-functional-theory-level precision with a prediction error of 3 meV Å−2 and enables large-scale surface exposure prediction with a 105-fold acceleration. Validation against computational and experimental data both show strong alignment. We applied SurFF for large-scale predictions of surface energy and Wulff shapes for over 6,000 intermetallic crystals, providing valuable data for the community. A foundation machine learning model, SurFF, enables DFT-accurate predictions of surface energies and morphologies in intermetallic catalysts, achieving over 105-fold acceleration for high-throughput materials screening.