文献类型：期刊文献

英文题名：Universal Descriptors of Propane Dehydrogenation Activity at Atomically Dispersed Metal-X Sites (X = O, S, C, and N): Machine Learning-Powered Inverse Catalyst Design

作者：Hu, Xiang-Long[1];Zhang, Rui[1];Lei, Ming[1];Zhou, Xing-Gui[1];Chen, De[2];Zhu, Yi-An[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, State Key Lab Green Chem Engn & Ind Catalysis, UNILAB, Shanghai 200237, Peoples R China;[2]Norwegian Univ Sci & Technol, Dept Chem Engn, N-9491 Trondheim, Norway

年份：2025

卷号：15

期号：12

起止页码：10561

外文期刊名：ACS CATALYSIS

收录：;EI(收录号：20252418579930);WOS:【SCI-EXPANDED(收录号:WOS:001503520800001)】；

基金：This work is supported by the National Key Research and Development Program of China (2024YFA1509901), the National Natural Science Foundation of China (91645122 and 22073027), and the research council of Norway through the innovation project for the industrial sector (340988, with Yara International).

语种：英文

外文关键词：atomically dispersed sites; inverse design; microkinetic analysis; machine learning; propanedehydrogenation; scaling relations; universal descriptors

摘要：Atomically dispersed metal-X (M-X) sites (X = O, S, C, and N) have shown great promise as active centers for propane dehydrogenation (PDH). In this work, the formation energies of adsorbed H at the X site (E H@X) and coadsorbed H&H at the M-X site (E H&H@M-X) are identified as two universal descriptors of the PDH activity at M-X sites by establishing their scaling relations with the rate-determining state energies. The derived volcano-shaped activity map is capable of providing a rational interpretation of experimentally reported catalysts. To rapidly predict E H@X and E H&H@M-X without DFT calculations, motif-specific features, including intrinsic elemental properties and the number of atoms characterizing the coordination environments, are constructed to train two extra-trees regression models to perform a multitiered virtual screening of 1,659,373 potential catalysts. Pt1Co1-Ga2O3 is found to show a higher activity than previously known Ir1-Ga2O3, because the substrate imposes a specific geometrical arrangement of the surface Pt, Co, Ga, and O atoms, which strengthens the binding of the rate-determining transition state through Lewis acid-base interactions and therefore imparts specific function to the highly active Pt-O site for PDH. These findings provide a general strategy for the inverse catalyst design by combining DFT-based microkinetic analysis with machine-learning algorithms.