Explainable artificial intelligence shines a light on catalysis: methods, applications, and future directions
TL;DR
It is shown that explainable artificial intelligence (XAI) provides a transformative approach by connecting model predictions to interpreting chemical descriptors, structural motifs, and reaction features, and it is reshaping data-driven catalysis from opaque prediction toward mechanism-informed catalyst design.
Abstract
Catalysis is fundamental to chemical manufacturing, energy conversion, and environmental remediation, yet the rational design of high-performance catalysts remains constrained by the complexity of catalysts, reaction environments, and multi-step reaction networks. However, while machine learning and deep learning have accelerated catalyst discovery and performance prediction, they often obscure the underlying physicochemical mechanisms, limiting mechanistic insights and practical reliability. Here, we show that explainable artificial intelligence (XAI) provides a transformative approach by connecting model predictions to interpre1 chemical descriptors, structural motifs, and reaction features. This review summarizes recent advances in XAI for catalysis, covering key methodologies, evaluation criteria, and representative applications across heterogeneous, homogeneous, and enzymatic catalysis. Particular attention is given to the identification of electronic, structural, and intrinsic atomic descriptors, as well as to the interpretation of active sites, dynamic speciation, reaction pathways, thermodynamics, and kinetics. By transforming black-box predictions into interpretable chemical insights, XAI is reshaping data-driven catalysis from opaque prediction toward mechanism-informed catalyst design.