Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments
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 lar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | The Innovation |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666675824000092 |
| _version_ | 1827349052361015296 |
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| author | Huan Ma Yueyue Jiao Wenping Guo Xingchen Liu Yongwang Li Xiaodong Wen |
| author_facet | Huan Ma Yueyue Jiao Wenping Guo Xingchen Liu Yongwang Li Xiaodong Wen |
| author_sort | Huan Ma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T00:23:28Z |
| format | Article |
| id | doaj.art-ff261e8be8964ca5aea719f1ac89ecd5 |
| institution | Directory Open Access Journal |
| issn | 2666-6758 |
| language | English |
| last_indexed | 2024-03-08T00:23:28Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Innovation |
| spelling | doaj.art-ff261e8be8964ca5aea719f1ac89ecd52024-02-16T04:30:22ZengElsevierThe Innovation2666-67582024-03-0152100571Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environmentsHuan Ma0Yueyue Jiao1Wenping Guo2Xingchen Liu3Yongwang Li4Xiaodong Wen5State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, Beijing 100049, China; National Energy Center for Coal to Liquids, Synfuels China Co., Ltd., Beijing 101400, ChinaState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, Beijing 100049, China; National Energy Center for Coal to Liquids, Synfuels China Co., Ltd., Beijing 101400, ChinaNational Energy Center for Coal to Liquids, Synfuels China Co., Ltd., Beijing 101400, China; Corresponding authorState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding authorState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, Beijing 100049, China; National Energy Center for Coal to Liquids, Synfuels China Co., Ltd., Beijing 101400, China; Beijing Advanced Innovation Center for Materials Genome Engineering, Industry−University Cooperation Base between Beijing Information S&T University and Synfuels China Co., Ltd., Beijing 100101, ChinaState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China; University of Chinese Academy of Sciences, Beijing 100049, China; National Energy Center for Coal to Liquids, Synfuels China Co., Ltd., Beijing 101400, China; Beijing Advanced Innovation Center for Materials Genome Engineering, Industry−University Cooperation Base between Beijing Information S&T University and Synfuels China Co., Ltd., Beijing 100101, China; Corresponding authorSolid 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.http://www.sciencedirect.com/science/article/pii/S2666675824000092 |
| spellingShingle | Huan Ma Yueyue Jiao Wenping Guo Xingchen Liu Yongwang Li Xiaodong Wen Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments The Innovation |
| title | Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments |
| title_full | Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments |
| title_fullStr | Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments |
| title_full_unstemmed | Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments |
| title_short | Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments |
| title_sort | machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments |
| url | http://www.sciencedirect.com/science/article/pii/S2666675824000092 |
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