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

Authors

Jun Yin, Honghao Chen, Jiangjie Qiu, Wentao Li, Peng He, Jiali Li, Iftekhar A Karimi, Xiaocheng Lan, Tiefeng Wang, Xiaonan Wang

Published in

Nature computational science. Sep 09, 2025. Epub Sep 09, 2025.

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.

PMID:
40926011
Bibliographic data and abstract were imported from PubMed on 10 Sep 2025.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.