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Updated: Jul 15, 2025
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
Published on: October 6, 2023
Topology-Determined Structural Genes Enable Data-Driven Discovery and Intelligent Design of Potential Metal Oxides
Chuan Zhou1, Chen Chen1, P Hu1,2
1State Key Laboratory of Green Chemical Engineering and Industrial Catalysis, Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai, 200237, China.
Abstract:
The identification of appropriate structural genes that influence the active-site configuration for a given reaction is critical for discovering potential catalysts with reduced reaction barriers. In this study, we introduce bulk-phase topology-derived tetrahedral descriptors as a means of expressing a catalyst's "material structural genes". We combine this approach with an interpretable machine learning model to accurately and efficiently predict the effective barrier associated with methane C-H bond cleavage across a wide range of metal oxides (MOs). These structural genes enable high-throughput catalyst screening for low-temperature methane activation and ultimately identify 13 candidate catalysts from a pool of 9095 MOs that are recommended for experimental synthesis. The topology-based method that we describe can also be extended to facilitate high-throughput catalyst screening and design for other dehydrogenation reactions.
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