Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors. Issue 12 (1st March 2022) - Yang, Ze; Gao, Wang
Abstract: At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure‐property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented. Abstract : This review briefly retrospects the applications of machine learning methods in various alloy catalyst systems, and summarizes several representative categories of descriptors for various alloy catalysts. Based on the machine learning scheme, not only the existing understanding of the physical picture in the study of heterogeneous catalysis is clarified, but also the significance of rational selection of descriptors is emphasized.