Article Computational Single-Atom Catalyst Database Empowers the Machine Learning Assisted Design of High-Performance Catalysts

Mingye Huang ; Ruiyang Shi ; Heng Liu ; Wenjun Ding ; Jiahang Fan ; Binghui Zhou ; Bo Da SAMURAI ORCID (National Institute for Materials Science) ; Zhengyang Gao ; Hao Li ; Weijie Yang

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Mingye Huang, Ruiyang Shi, Heng Liu, Wenjun Ding, Jiahang Fan, Binghui Zhou, Bo Da, Zhengyang Gao, Hao Li, Weijie Yang. Computational Single-Atom Catalyst Database Empowers the Machine Learning Assisted Design of High-Performance Catalysts. Journal of Physical Chemistry C. 2025, 129 (10), 5043-5053. https://doi.org/10.1021/acs.jpcc.5c00491

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(abstract)

The data-driven strategy has emerged as an important approach for the rapid screening of high-performance single-atom catalysts (SACs). However, the lack of a comprehensive SACs database seriously hinders the widespread application of this strategy. Herein, we construct a public SACs database comprising 1197 samples via doping nonmetallic atoms (B, N. O, P, and S) in the coordination environment and regulating 3d metal centers (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn). Based on density functional theory calculations, the electronic structural properties (i.e., Bader charge and d-band center) and binding energies are obtained. According to the binding energy calculations, 657 stable catalyst configurations are identified. Subsequently, the corresponding adsorption energies for O2, O, and NO are calculated. Moreover, machine learning (ML) models, specifically extreme gradient boosting regression

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Keyword: High-Performance Catalysts

Date published: 2025-03-13

Publisher: American Chemical Society (ACS)

Journal:

  • Journal of Physical Chemistry C (ISSN: 19327447) vol. 129 issue. 10 p. 5043-5053

Funding:

  • National Natural Science Foundation of China 52006073
  • National Natural Science Foundation of China 52176104

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1021/acs.jpcc.5c00491

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Updated at: 2025-12-19 16:30:28 +0900

Published on MDR: 2025-12-19 14:11:49 +0900

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