{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:56:38Z","timestamp":1775004998841,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["No. 2022ZD0117501"],"award-info":[{"award-number":["No. 2022ZD0117501"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tsinghua University Initiative Scientific Research Program. Scientific Research Innovation Capability Support Project for Young Faculty"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-025-00839-0","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T09:05:02Z","timestamp":1757408702000},"page":"782-792","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["SurFF: a foundation model for surface exposure and morphology across intermetallic crystals"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8993-3178","authenticated-orcid":false,"given":"Jun","family":"Yin","sequence":"first","affiliation":[]},{"given":"Honghao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jiangjie","family":"Qiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3002-7536","authenticated-orcid":false,"given":"Wentao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peng","family":"He","sequence":"additional","affiliation":[]},{"given":"Jiali","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7122-0578","authenticated-orcid":false,"given":"Iftekhar A.","family":"Karimi","sequence":"additional","affiliation":[]},{"given":"Xiaocheng","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Tiefeng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9775-2417","authenticated-orcid":false,"given":"Xiaonan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"839_CR1","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1002\/anie.201208487","volume":"52","author":"JK N\u00f8rskov","year":"2013","unstructured":"N\u00f8rskov, J. K. & Bligaard, T. The catalyst genome. Angew. Chem. Int. Ed. 52, 776\u2013777 (2013).","journal-title":"Angew. Chem. Int. Ed."},{"key":"839_CR2","doi-asserted-by":"crossref","unstructured":"N\u00f8rskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Fundamental concepts in heterogeneous catalysis (Wiley, 2015).","DOI":"10.1002\/9781118892114"},{"key":"839_CR3","first-page":"2778","volume":"2","author":"KJ Jenewein","year":"2022","unstructured":"Jenewein, K. J., Akkoc, G. D., Korm\u00e1nyos, A. & Cherevko, S. Automated high-throughput activity and stability screening of electrocatalysts. Chem. Catal. 2, 2778\u20132794 (2022).","journal-title":"Chem. Catal."},{"key":"839_CR4","doi-asserted-by":"publisher","first-page":"4401","DOI":"10.1021\/acs.jpclett.9b01428","volume":"10","author":"S Back","year":"2019","unstructured":"Back, S. et al. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J. Phys. Chem. Lett. 10, 4401\u20134408 (2019).","journal-title":"J. Phys. Chem. Lett."},{"key":"839_CR5","doi-asserted-by":"publisher","unstructured":"Fung, V., Hu, G., Ganesh, P. & Sumpter, B. G. Machine learned features from density of states for accurate adsorption energy prediction. Nat. Commun. https:\/\/doi.org\/10.1038\/s41467-020-20342-6 (2021).","DOI":"10.1038\/s41467-020-20342-6"},{"key":"839_CR6","doi-asserted-by":"publisher","first-page":"16963","DOI":"10.1021\/acs.jpcc.1c02890","volume":"125","author":"J Lee","year":"2021","unstructured":"Lee, J. & Jinnouchi, R. Machine learning-based screening of highly stable and active ternary Pt alloys for oxygen reduction reaction. J. Phys. Chem. C 125, 16963\u201316974 (2021).","journal-title":"J. Phys. Chem. C"},{"key":"839_CR7","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1038\/s41586-020-2242-8","volume":"581","author":"M Zhong","year":"2020","unstructured":"Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178\u2013183 (2020).","journal-title":"Nature"},{"key":"839_CR8","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41929-018-0142-1","volume":"1","author":"K Tran","year":"2018","unstructured":"Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 1, 696\u2013703 (2018).","journal-title":"Nat. Catal."},{"key":"839_CR9","doi-asserted-by":"publisher","first-page":"4993","DOI":"10.1021\/jacs.3c14495","volume":"146","author":"X Ge","year":"2024","unstructured":"Ge, X. et al. Atomic design of alkyne semihydrogenation catalysts via active learning. J. Am. Chem. Soc. 146, 4993\u20135004 (2024).","journal-title":"J. Am. Chem. Soc."},{"key":"839_CR10","doi-asserted-by":"publisher","first-page":"064239","DOI":"10.1088\/0953-8984\/20\/6\/064239","volume":"20","author":"G Jones","year":"2008","unstructured":"Jones, G., Bligaard, T., Abild-Pedersen, F. & N\u00f8rskov, J. K. Using scaling relations to understand trends in the catalytic activity of transition metals. J. Phys. Condens. Matter 20, 064239 (2008).","journal-title":"J. Phys. Condens. Matter"},{"key":"839_CR11","doi-asserted-by":"publisher","first-page":"13684","DOI":"10.1021\/ja5051555","volume":"136","author":"AB Getsoian","year":"2014","unstructured":"Getsoian, A. B., Zhai, Z. & Bell, A. T. Band-gap energy as a descriptor of catalytic activity for propene oxidation over mixed metal oxide catalysts. J. Am. Chem. Soc. 136, 13684\u201313697 (2014).","journal-title":"J. Am. Chem. Soc."},{"key":"839_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.80","volume":"3","author":"R Tran","year":"2016","unstructured":"Tran, R. et al. Surface energies of elemental crystals. Sci. Data 3, 160080 (2016).","journal-title":"Sci. Data"},{"key":"839_CR13","doi-asserted-by":"publisher","first-page":"6149","DOI":"10.1021\/acscatal.0c01005","volume":"10","author":"R Cheula","year":"2020","unstructured":"Cheula, R., Maestri, M. & Mpourmpakis, G. Modeling morphology and catalytic activity of nanoparticle ensembles under reaction conditions. ACS Catal. 10, 6149\u20136158 (2020).","journal-title":"ACS Catal."},{"key":"839_CR14","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.1063\/1.1735524","volume":"31","author":"JJ Gilman","year":"1960","unstructured":"Gilman, J. J. Direct measurements of the surface energies of crystals. J. Appl. Phys. 31, 2208\u20132218 (1960).","journal-title":"J. Appl. Phys."},{"key":"839_CR15","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1103\/PhysRevLett.70.1643","volume":"70","author":"D Eaglesham","year":"1993","unstructured":"Eaglesham, D., White, A., Feldman, L., Moriya, N. & Jacobson, D. Equilibrium shape of Si. Phys. Rev. Lett. 70, 1643 (1993).","journal-title":"Phys. Rev. Lett."},{"key":"839_CR16","doi-asserted-by":"publisher","first-page":"3399","DOI":"10.1021\/nl2018146","volume":"11","author":"E Ringe","year":"2011","unstructured":"Ringe, E., Van Duyne, R. P. & Marks, L. Wulff construction for alloy nanoparticles. Nano Lett. 11, 3399\u20133403 (2011).","journal-title":"Nano Lett."},{"key":"839_CR17","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1126\/science.adn0558","volume":"383","author":"K Zhang","year":"2024","unstructured":"Zhang, K. et al. Spin-mediated promotion of Co catalysts for ammonia synthesis. Science 383, 1357\u20131363 (2024).","journal-title":"Science"},{"key":"839_CR18","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1038\/s41929-023-01069-1","volume":"7","author":"Y Li","year":"2024","unstructured":"Li, Y. et al. Electrolyte-assisted polarization leading to enhanced charge separation and solar-to-hydrogen conversion efficiency of seawater splitting. Nat. Catal. 7, 77\u201388 (2024).","journal-title":"Nat. Catal."},{"key":"839_CR19","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.progsurf.2005.09.004","volume":"80","author":"CR Henry","year":"2005","unstructured":"Henry, C. R. Morphology of supported nanoparticles. Prog. Surf. Sci. 80, 92\u2013116 (2005).","journal-title":"Prog. Surf. Sci."},{"key":"839_CR20","doi-asserted-by":"publisher","first-page":"4742","DOI":"10.1021\/acs.jcim.9b00550","volume":"59","author":"A Palizhati","year":"2019","unstructured":"Palizhati, A., Zhong, W., Tran, K., Back, S. & Ulissi, Z. W. Toward predicting intermetallics surface properties with high-throughput DFT and convolutional neural networks. J. Chem. Inf. Model. 59, 4742\u20134749 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"839_CR21","doi-asserted-by":"publisher","first-page":"10142","DOI":"10.1021\/acs.chemrev.0c01111","volume":"121","author":"OT Unke","year":"2021","unstructured":"Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142\u201310186 (2021).","journal-title":"Chem. Rev."},{"key":"839_CR22","doi-asserted-by":"publisher","first-page":"6059","DOI":"10.1021\/acscatal.0c04525","volume":"11","author":"L Chanussot","year":"2021","unstructured":"Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 6059\u20136072 (2021).","journal-title":"ACS Catal."},{"key":"839_CR23","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1021\/acscatal.2c05426","volume":"13","author":"R Tran","year":"2023","unstructured":"Tran, R. et al. The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts. ACS Catal. 13, 3066\u20133084 (2023).","journal-title":"ACS Catal."},{"key":"839_CR24","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1038\/s43588-024-00636-1","volume":"4","author":"C Ben Mahmoud","year":"2024","unstructured":"Ben Mahmoud, C., Gardner, J. L. A. & Deringer, V. L. Data as the next challenge in atomistic machine learning. Nat. Comput. Sci. 4, 384\u2013387 (2024).","journal-title":"Nat. Comput. Sci."},{"key":"839_CR25","doi-asserted-by":"publisher","first-page":"20120476","DOI":"10.1098\/rsta.2012.0476","volume":"372","author":"R Peverati","year":"2014","unstructured":"Peverati, R. & Truhlar, D. G. Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics. Phil. Trans. R. Soc. A 372, 20120476 (2014).","journal-title":"Phil. Trans. R. Soc. A"},{"key":"839_CR26","doi-asserted-by":"publisher","unstructured":"Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. https:\/\/doi.org\/10.1063\/1.4812323 (2013).","DOI":"10.1063\/1.4812323"},{"key":"839_CR27","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/s41524-019-0153-8","volume":"5","author":"T Lookman","year":"2019","unstructured":"Lookman, T., Balachandran, P. V., Xue, D. & Yuan, R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. NPJ Comput. Mater. 5, 21 (2019).","journal-title":"NPJ Comput. Mater."},{"key":"839_CR28","first-page":"1","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren, P. et al. A survey of deep active learning. ACM Comput. Surveys 54, 1\u201340 (2021).","journal-title":"ACM Comput. Surveys"},{"key":"839_CR29","doi-asserted-by":"publisher","first-page":"1900029","DOI":"10.1002\/aisy.201900029","volume":"1","author":"J Li","year":"2019","unstructured":"Li, J. et al. Deep learning accelerated gold nanocluster synthesis. Adv. Intel. Syst. 1, 1900029 (2019).","journal-title":"Adv. Intel. Syst."},{"key":"839_CR30","unstructured":"Liao, Y.-L., Wood, B. M., Das, A. & Smidt, T. EquiformerV2: improved equivariant transformer for scaling to higher-degree representations. In Proc. 12th International Conference on Learning Representations (ICLR, 2024)."},{"key":"839_CR31","unstructured":"Zitnick, L. et al. Spherical channels for modeling atomic interactions. In Proc. 36th Conference on Neural Information Processing Systems (NeurIPS, 2022)."},{"key":"839_CR32","unstructured":"DeepSeek, A. I. et al. DeepSeek-V2: a strong, economical, and efficient mixture-of-experts language model. Preprint at https:\/\/arxiv.org\/abs\/2405.04434 (2024)."},{"key":"839_CR33","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1039\/D3SC05460H","volume":"15","author":"X Lan","year":"2024","unstructured":"Lan, X., Wang, Y., Liu, B., Kang, Z. & Wang, T. Thermally induced intermetallic Rh1Zn1 nanoparticles with high phase-purity for highly selective hydrogenation of acetylene. Chem. Sci. 15, 1758\u20131768 (2024).","journal-title":"Chem. Sci."},{"key":"839_CR34","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1038\/s41929-021-00578-1","volume":"4","author":"J Wang","year":"2021","unstructured":"Wang, J. et al. Redirecting dynamic surface restructuring of a layered transition metal oxide catalyst for superior water oxidation. Nat. Catal. 4, 212\u2013222 (2021).","journal-title":"Nat. Catal."},{"key":"839_CR35","doi-asserted-by":"publisher","first-page":"4696","DOI":"10.1021\/acscatal.2c00583","volume":"12","author":"K Feng","year":"2022","unstructured":"Feng, K. et al. Dual functionalized interstitial N atoms in Co3Mo3N enabling CO2 activation. ACS Catal. 12, 4696\u20134706 (2022).","journal-title":"ACS Catal."},{"key":"839_CR36","doi-asserted-by":"publisher","first-page":"118719","DOI":"10.1016\/j.apcatb.2020.118719","volume":"267","author":"A Pajares","year":"2020","unstructured":"Pajares, A. et al. Critical effect of carbon vacancies on the reverse water gas shift reaction over vanadium carbide catalysts. Appl. Catal. B 267, 118719 (2020).","journal-title":"Appl. Catal. B"},{"key":"839_CR37","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1021\/acs.jcim.3c00142","volume":"63","author":"RY Sanspeur","year":"2023","unstructured":"Sanspeur, R. Y., Heras-Domingo, J., Kitchin, J. R. & Ulissi, Z. WhereWulff: a semiautonomous workflow for systematic catalyst surface reactivity under reaction conditions. J. Chem. Inf. Model. 63, 2427\u20132437 (2023).","journal-title":"J. Chem. Inf. Model."},{"key":"839_CR38","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.susc.2013.05.016","volume":"617","author":"W Sun","year":"2013","unstructured":"Sun, W. & Ceder, G. Efficient creation and convergence of surface slabs. Surf. Sci. 617, 53\u201359 (2013).","journal-title":"Surf. Sci."},{"key":"839_CR39","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.commatsci.2012.10.028","volume":"68","author":"SP Ong","year":"2013","unstructured":"Ong, S. P. et al. Python materials genomics (PyMatGen): a robust, open-source Python library for materials analysis. Comput. Mater. Sci. 68, 314\u2013319 (2013).","journal-title":"Comput. Mater. Sci."},{"key":"839_CR40","unstructured":"Konyushkova, K., Sznitman, R. & Fua, P. Learning active learning from data. In Proc. 31st Conference on Neural Information Processing Systems (NeurIPS, 2017)"},{"key":"839_CR41","unstructured":"Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Proc. 31st Conference on Neural Information Processing Systems (NeurIPS, 2017)."},{"key":"839_CR42","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-014-0781-x","volume":"113","author":"Y Yang","year":"2015","unstructured":"Yang, Y., Ma, Z., Nie, F., Chang, X. & Hauptmann, A. G. Multi-class active learning by uncertainty sampling with diversity maximization. Int. J. Comput. Vision 113, 113\u2013127 (2015).","journal-title":"Int. J. Comput. Vision"},{"key":"839_CR43","unstructured":"Passaro, S. & Zitnick, C. L. Reducing SO(3) convolutions to SO(2) for efficient equivariant GNNs. In Proc. 40th International Conference on Machine Learning (PMLR, 2023)."},{"key":"839_CR44","unstructured":"Geiger, M. & Smidt, T. e3nn: Euclidean neural networks. Preprint at https:\/\/arxiv.org\/abs\/2207.09453 (2022)."},{"key":"839_CR45","unstructured":"Klicpera, J., Becker, F. & G\u00fcnnemann, S. GemNet: universal directional graph neural networks for molecules. In Proc. 35th Conference on Neural Information Processing Systems (NeurIPS, 2021)."},{"key":"839_CR46","unstructured":"Ying, C. et al. Do transformers really perform badly for graph representation? In Proc. 35th Conference on Neural Information Processing Systems (NeurIPS, 2021)."},{"key":"839_CR47","doi-asserted-by":"publisher","unstructured":"Yin, J. et al. SurFF_coredatafiles. figshare https:\/\/doi.org\/10.6084\/m9.figshare.26395756 (2025).","DOI":"10.6084\/m9.figshare.26395756"},{"key":"839_CR48","doi-asserted-by":"publisher","unstructured":"Yin, J. et al. SurFF: 1.0. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15480651 (2025).","DOI":"10.5281\/zenodo.15480651"},{"key":"839_CR49","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1038\/s41929-022-00757-8","volume":"5","author":"Y Ji","year":"2022","unstructured":"Ji, Y. et al. Selective CO-to-acetate electroreduction via intermediate adsorption tuning on ordered Cu\u2013Pd sites. Nat. Catal. 5, 251\u2013258 (2022).","journal-title":"Nat. Catal."}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00839-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00839-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00839-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T19:47:58Z","timestamp":1759175278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00839-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["839"],"URL":"https:\/\/doi.org\/10.1038\/s43588-025-00839-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4863775\/v1","asserted-by":"object"}]},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,9]]},"assertion":[{"value":"5 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}