Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction

Abstract

Abstract In the past decades, machine learning (ML) has impacted the field of electrocatalysis. Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design. Hence, significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO2 reduction. This review discusses recent applications of ML to discover, design, and optimize novel electrocatalysts. First, insights into ML aided in accelerating calculation are presented. Then, ML aided in the rational design of the electrocatalyst is introduced, including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model. Finally, the opportunities and future challenges are summarized for the future design of electrocatalyst for CO2 reduction with the assistance of ML.

Keywords

• CO2 reduction reaction
• DFT calculation
• electrocatalyst
• machine learning
• rational design