Toward Next-Generation Heterogeneous Catalysts:Empowering Surface Reactivity Prediction with Machine Learning  

通往下一代多相催化剂:机器学习助力表面反应性预测

作　　者：Xinyan Liu Hong-Jie Peng 刘芯言;彭翃杰

机构地区：[1]Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 611731,China

出　　处：《Engineering》2024年第8期25-44,共20页工程（英文）

基　　金：supported by the National Natural Science Foundation of China(22109020 and 22109082).

摘　　要：Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility.Computational high-throughput screening presents a viable solution to this challenge,as machine learning(ML)has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information.This review focuses on recent progress in applying ML in adsorption energy prediction,which predominantly quantifies the catalytic potential of a solid catalyst.ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed.At the end of the review,an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied.We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and,ultimately,reshape the chemical industry and energy landscape.

关 键 词：Machine learning Heterogeneous catalysis CHEMISORPTION Theoretical simulation Materials design High-throughput screening 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O643.36[自动化与计算机技术—控制科学与工程]