Machine Learning Assisted Understanding and Discovery of CO2 Reduction Reaction Electrocatalyst

Electrochemical CO2 reduction reaction (CO2RR) is an important process which is a potential way to recycle excessive CO2 in the atmosphere. Although the electrocatalyst is the key toward efficient CO2RR, the progress of discovering effective catalysts is lagging with current methods. Because of the cost and time efficiency of the modern machine learning (ML) algorithm, an increasing number of researchers have applied ML to accelerate the screening of suitable catalysts and to deepen our understanding in the mechanism. Hence, we reviewed recent applications of ML in the research of CO2RR by the types of electrocatalyst. An introduction on the general methodology and a discussion on the pros and cons for such applications are included.

Automated summary

Electrochemical CO2 reduction reaction (CO2RR) is a significant process for recycling excessive CO2 in the atmosphere. However, the discovery of efficient catalysts for CO2RR is currently lagging behind due to limitations in current methods. To overcome this, researchers have increasingly used modern machine learning (ML) algorithms to accelerate catalyst screening and deepen our understanding of the mechanism. In this review, we examine recent applications of ML in CO2RR research, categorizing them by the types of electrocatalysts, and provide an introduction to the general methodology as well as a discussion on the pros and cons of such applications.