A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction

Machine learning (ML) with its indigenous predicting ability has been influential in the current scientific world and has enabled a paradigm shift in the field of CO2 reduction reaction (CO2RR). In this perspective, current research progress of ML approaches in heterogeneous electrocatalytic CO2RR has been demonstrated. The important findings related to the ML systems comprising features, output descriptors, and ML models have been summarized. Further, the opportunities and challenges in using the state-of-the-art ML methodologies along with the ways of circumventing those challenges are discussed. Finally, the interpretation of black box ML models and extensive usages of interpretable glass box and gray box models for CO2RR are encouraged for obtaining proper physical interpretations. The future directions on utilizing several such evolving ML methods to predict catalytic activity descriptors can help in a broader way to explore novel and efficient heterogeneous CO2RR and other similar catalytic reactions.