Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors

Abstract

The complexity and dynamics of catalytic systems make it challenging to study the catalysts and catalytic reactions. Fortunately, the advance of machine learning (ML) has made descriptor-based catalyst screening and rational design a mainstream research approach. Herein, the spectroscopic descriptors reported in recent years are highlighted in the field of catalysis. Both vibrational spectra and X-ray absorption spectra have demonstrated strong ability to predict catalytic structures and properties. Through several cases, the interpretable ML models based on spectroscopic descriptors are discussed to reveal physical knowledge and catalytic mechanism and to exhibit superiority in transfer learning tasks and imperfect data scenarios. Finally, in this Viewpoint, we illustrate the challenges in the research field of interpretable ML models with spectroscopic descriptors and provide perspectives.