Machine Learning-Accelerated Advancements in Electrocatalytic CO2 Reduction
TL;DR
This review summarizes the latest applications of ML in CO2RR technology, focusing on ML applications in catalyst performance prediction, key descriptor discovery, catalyst screening, mechanistic investigation, and optimization of synthesis parameters.
Abstract
With growing concerns over climate change, electrochemical CO2 reduction (CO2RR) has attracted significant attention for converting CO2 into valuable products under mild conditions, but catalyst design remains challenging because traditional, experiment-driven approaches are inefficient and time-consuming. Recently, machine learning (ML), as an advanced data-driven technology, has been widely applied to catalyst design and performance prediction, providing new approaches for rapid screening, structural optimization, and mechanistic exploration of catalysts. Building on these recent advances, this review summarizes the latest applications of ML in CO2RR technology, focusing on ML applications in catalyst performance prediction, key descriptor discovery, catalyst screening, mechanistic investigation, and optimization of synthesis parameters. Additionally, this review discusses the challenges faced by ML in CO2RR catalyst development, including data scarcity, poor model generalization, and difficulties in multiobjective optimization, while looking ahead to future directions such as transfer learning, the establishment of automated synthesis platforms, and the integration of multiobjective optimization frameworks, thereby providing a roadmap for the rational design of next-generation CO2RR catalysts that aims to guide the development of data-driven catalyst design approach.