Zhou Chuan, Chen Chen, Hu P, Wang Haifeng
State 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.
School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast, BT9 5AG, U.K.
J Am Chem Soc. 2023 Oct 11;145(40):21897-21903. doi: 10.1021/jacs.3c06166. Epub 2023 Sep 27.
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.
识别影响给定反应活性位点构型的合适结构基因对于发现具有降低反应势垒的潜在催化剂至关重要。在本研究中，我们引入了体相拓扑衍生的四面体描述符，作为表达催化剂“材料结构基因”的一种手段。我们将这种方法与可解释的机器学习模型相结合，以准确、高效地预测与广泛的金属氧化物（MO）上甲烷C-H键裂解相关的有效势垒。这些结构基因能够实现高通量催化剂筛选，用于低温甲烷活化，并最终从9095种MO中确定13种候选催化剂，推荐进行实验合成。我们所描述的基于拓扑的方法还可以扩展，以促进其他脱氢反应的高通量催化剂筛选和设计。
