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O: Fachverband Oberflächenphysik
O 55: Poster Session IV: Poster to Mini-Symposium: Machine learning applications in surface science II
O 55.7: Poster
Tuesday, March 2, 2021, 13:30–15:30, P
Single-Atom Alloy Catalysts Designed by First-Principles Calculations and Artificial Intelligence — Zhong-Kang Han1, Debalaya Sarker1, Runhai Ouyang2, Aliaksei Mazheika3, Yi Gao4, and •Sergey V. Levchenko1 — 1Skoltech, Moscow, RU — 2Shanghai University, CN — 3Technische Universitaet Berlin, DE — 4Shanghai Advanced Research Institute, Chinese Academy of Sciences, CN
Single-atom metal alloy catalysts (SAACs) have recently become a very active new frontier in catalysis research. However, discovery of new SAACs is hindered by the lack of fast yet reliable prediction of the catalytic properties of the sheer number of candidate materials. In this work, we address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting high efficiency of the experimentally studied SAACs, we identify more than two hundred yet unreported promising candidates. Some of these new candidates are predicted to exhibit even higher stability and efficiency than the reported ones. Our study demonstrates the importance of breaking linear relationships to avoid bias in catalysis design, as well as provides a recipe for selecting best candidate materials from hundreds of thousands of transition-metal SAACs for various applications. In addition, we demosntrate how the data-mining approach subgroup discovery can be used to obtain a qualitative understanding of complex symbolic regression models.