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

Abstract

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 CO₂ reduction (CO₂RR) using ML, which includes three iterative cycles: "experimental test; ML analysis; prediction and redesign". Cu catalysts are known for CO₂RR to obtain a range of products including C₁ (CO, HCOOH, CH₄, CH₃OH) and C₂₊ (C₂H₄, C₂H₆, C₂H₅OH, C₃H₇OH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CO₂RR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C₂₊ 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.