Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis  

作　　者：Xiran Cheng Chenyu Wu Jiayan Xu Yulan Han Wenbo Xie P.Hu 

机构地区：[1]School of Physical Science and Technology,ShanghaiTech University,Shanghai 201210,China [2]Key Laboratory of Mesoscopic Chemistry of MOE,School of Chemistry and Chemical Engineering,Nanjing University,Nanjing 210023,China [3]School of Chemistry and Chemical Engineering,The Queen’s University of Belfast,Belfast BT95AG,U.K.

出　　处：《Precision Chemistry》2024年第11期570-586,共17页精准化学（英文）

基　　金：the NKRDPC(2021YFA1500700)and NSFC(92045303).X.C.is grateful for financial support from ShanghaiTech University.

摘　　要：This Perspective explores the integration of machine learning potentials(MLPs)in the research of heterogeneous catalysis,focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes.MLPs utilize extensive databases from high-throughput density functional theory(DFT)calculations to train models that predict atomic configurations,energies,and forces with near-DFT accuracy.These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods.Coupled with global optimization algorithms,MLPs enable systematic investigations across vast structural spaces,making substantial contributions to the modeling of catalyst surface structures under reactive conditions.The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis.The prevailing challenges faced by this approach are also discussed.

关 键 词：heterogeneous catalysis machine learning potential global optimizations active sites structure prediction 

分 类 号：O643.3[理学—物理化学]