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.
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Data availability
All of the datasets used in this work are also available via figshare47. The minimum data that are necessary to interpret, verify and extend the research in the article are provided via SurFF_CoreDataFiles.zip, which contains the core data files for the SurFF project, allowing the user to run the SurFF prediction for intermetalic crystals. All of this work’s data are provided via SurFF_DataFiles.zip, which contains all of the generated DFT datasets. Please refer to the GitHub link below for more detailed instructions on how to reproduce the main results and apply SurFF to prediction. Source data are provided with this paper.
Code availability
All of the codes in this work could be retrieved from GitHub repository via https://github.com/Long1Corn/SurFF or Zenodo (ref. 48). A step-by-step guide to use SurFF for prediction and a web user interface are also provided.
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Acknowledgements
This work is supported by the National Key R&D Program of China (grant no. 2022ZD0117501), the Scientific Research Innovation Capability Support Project for Young Faculty (grant no. ZYGXQNJSKYCXNLZCXM-E7), the Tsinghua University Initiative Scientific Research Program and the Carbon Neutrality and Energy System Transformation (CNEST) Program led by Tsinghua University. The primary affiliation of J.Y. is the Department of Chemical and Biomolecular Engineering, National University of Singapore.
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X.W. supervised the project. J.Y., X.W. and I.A.K. conceptualized this project. J.Y., H.C. and P.H. performed DFT calculations. J.Y., W.L. and J.L. trained the machine learning models. J.Q. collected experimental data from literature. X.L. and T.W. collected experimental data by experiments. All authors contributed towards writing the manuscript.
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Nature Computational Science thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.
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Supplementary Information
Supplementary Sections 1–7, Tables 1–18 and 20–21, and Figs. 1–39.
Supplementary Table 19
Experimental observations from the literature. Observed surfaces from the literature are compared with SurFF-predicted surface facets, energies and exposure values for selected intermetallics, highlighting qualitative matches.
Source data
Source Data Fig. 2
All data points to generate Fig. 2c; Source data for Fig. 2c–e.
Source Data Fig. 3
Source data for Fig. 3a–c,e,g,h; Source data to draw 3D crystals for Fig. 3d,f.
Source Data Fig. 4
Source data to draw 3D crystals for Fig. 4b.
Source Data Fig. 5
Source data for Fig. 5b.
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Yin, J., Chen, H., Qiu, J. et al. SurFF: a foundation model for surface exposure and morphology across intermetallic crystals. Nat Comput Sci 5, 782–792 (2025). https://doi.org/10.1038/s43588-025-00839-0
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DOI: https://doi.org/10.1038/s43588-025-00839-0
