Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO₂ Reduction

Discovery and optimization of new catalysts can be potentially accelerated by efficient data analysis using machine-learning (ML). In this paper, we record the process of searching for additives in the electrochemical deposition of Cu catalysts for CO 2 reduction (CO 2 RR) using ML, which includes three iterative cycles: “experimental test; ML analysis; prediction and redesign”. Cu catalysts are known for CO 2 RR to obtain a range of products including C 1 (CO, HCOOH, CH 4, CH 3 OH) and C 2+ (C 2 H 4, C 2 H 6, C 2 H 5 OH, C 3 H 7 OH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CO 2 RR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C 2+ products. This catalyst discovery process highlights the potential of ML to accelerate material development by efficiently extracting information from a limited number of experimental data.