{"title":"Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments","authors":"Huan Ma, Yueyue Jiao, Wenping Guo, Xingchen Liu, Yongwang Li, Xiaodong Wen","doi":"10.1016/j.xinn.2024.100571","DOIUrl":null,"url":null,"abstract":"<p>Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of <em>ab initio</em> calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at <em>ab initio</em> accuracy using an approach that combines the bond valence model, Gaussian process regression, and <em>ab initio</em> thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"55 1","pages":""},"PeriodicalIF":33.2000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Innovation","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1016/j.xinn.2024.100571","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of ab initio calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at ab initio accuracy using an approach that combines the bond valence model, Gaussian process regression, and ab initio thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.
固体表面通常在反应条件下通过与环境的粒子交换达到热力学平衡。了解其功能的前提是详细了解工作条件下的表面成分和原子几何形状。由于多元素固体中可能存在大量的米勒指数和端点,对可靠的表面能估算所需的组成和构象空间进行广泛采样超出了 ab initio 计算的范围。在此,我们以碳化铁在不同碳化学势环境中的情况为例,证明了多元素固体在反应条件下的稳定表面成分和几何形状(涉及较大的成分和构象空间),可以通过结合键价模型、高斯过程回归和 ab initio 热力学的方法,以 ab initio 的精度进行预测。确定工作条件下表面的原子结构为确定多元素催化剂在异相催化中的真正活性位点铺平了道路。
期刊介绍:
The Innovation is an interdisciplinary journal that aims to promote scientific application. It publishes cutting-edge research and high-quality reviews in various scientific disciplines, including physics, chemistry, materials, nanotechnology, biology, translational medicine, geoscience, and engineering. The journal adheres to the peer review and publishing standards of Cell Press journals.
The Innovation is committed to serving scientists and the public. It aims to publish significant advances promptly and provides a transparent exchange platform. The journal also strives to efficiently promote the translation from scientific discovery to technological achievements and rapidly disseminate scientific findings worldwide.
Indexed in the following databases, The Innovation has visibility in Scopus, Directory of Open Access Journals (DOAJ), Web of Science, Emerging Sources Citation Index (ESCI), PubMed Central, Compendex (previously Ei index), INSPEC, and CABI A&I.