Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO 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 reduction (CORR) using ML, which includes three iterative cycles: "experimental test; ML analysis; prediction and redesign". Cu catalysts are known for CORR to obtain a range of products including C (CO, HCOOH, CH, CHOH) and C (CH, CH, CHOH, CHOH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CORR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C 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.