厦门大学汪骋团队报道了机器学习指导下的CO2还原铜催化剂添加剂的发现与优化。相关研究成果发表与2021年4月12日出版的《美国化学会杂志》。

利用机器学习（ML）进行有效的数据分析，有可能加速新催化剂的发现和优化。

该文记录了用最大似然法（ML）在电化学沉积铜催化剂CO2还原（CO2RR）过程中寻找添加剂的过程，包括“实验测试；最大似然分析；预测和重新设计”三个迭代循环。众所周知，铜催化剂用于制备一系列产品，包括C1（CO、HCOOH、CH4、CH3OH）和C2+（C2H4、C2H6、C2H5OH、C3H7OH）。

在催化剂制备过程中，添加剂对催化剂的形貌和表面结构造成的细微变化会导致CO2RR选择性的显著改变。经过几个ML循环后，研究人员得到了对CO、HCOOH和C2+产物有选择性的催化剂。该催化剂发现过程凸显了ML通过有效地从有限的实验数据中提取信息来加速材料开发的潜力。

据悉，新催化剂的发现和优化可以通过高效的数据分析和机器学习(ML)来加速。

附：英文原文

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

Author: Ying Guo, Xinru He, Yuming Su, Yiheng Dai, Mingcan Xie, Shuangli Yang, Jiawei Chen, Kun Wang, Da Zhou, Cheng Wang

Issue&Volume: April 12, 2021

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.

DOI: 10.1021/jacs.1c00339

Source: https://pubs.acs.org/doi/10.1021/jacs.1c00339