SurFF: Universal Model for Surface Exposure and Synthesizability Across Intermetallic Crystals

Catalysts are crucial in industrial processes, significantly enhancing reaction efficiency. With approximately 90% of industrial reactions occurring on surfaces, the role of heterogeneous catalysts is paramount. Accurate surface exposure prediction is vital for heterogeneous catalyst design but is hindered by the high costs of experimental and computational methods. Here, we introduce a universal force field-based model for predicting surface exposure and synthesizability (SurFF) across intermetallic crystals, essential materials for heterogeneous catalysts. We created a comprehensive intermetallic surface database using a data-efficient active learning method and high-throughput density functional theory (DFT) calculations, encompassing 12,553 unique surfaces and 344,200 single points. SurFF achieves DFT-level precision with a prediction error of 3.0 meV/Ų and enables large-scale surface exposure prediction, an impractical task for DFT methods, through a 10⁵-fold acceleration. Validation against computational and experimental data both shows strong alignment. We applied SurFF for large-scale predictions on over 6,000 intermetallic crystals, providing valuable data for the community. Demonstrating transferability to diverse crystal properties, SurFF is a robust tool for advancing catalyst design, representing a significant step toward large-scale catalyst discovery models.