Machine-learning-assisted optimization of a single-atom coordination environment for accelerated fenton catalysis

• Main Authors: Fu, Haoyang, Li, Ke, Zhang, Chenfei, Zhang, Jianghong, Liu, Jiyuan, Chen, Xi, Chen, Guoliang, Sun, Yongyang, Li, Shuzhou, Ling, Lan
• 其他作者: School of Materials Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2023
• 主題: Engineering::Materials; Machine Learning; Single-Atom Catalysts
• 在線閱讀: https://hdl.handle.net/10356/171141
• 機構: Nanyang Technological University

Machine learning (ML) algorithms will be enablers in revolutionizing traditional methods of materials optimization. Here, we broaden the use of ML to assist the construction of Fenton-like single-atom catalysts (SACs) by developing a methodology including model building, training, and prediction. Our approach can efficiently extract synthesis parameters that exert a substantial influence on Fenton activity and accurately predict the phenol degradation rate k of SACs with a mean error of ±0.018 min-1. The extended synthesis window with accelerated learning enables the realization that the heating temperatures during SAC synthesis significantly influence the Fe-N coordination number, which ultimately dictates their performance. Through ML-guided optimization, a highly efficient SAC dominated by Fe-N5 sites with exceptional Fenton activity (k = 0.158 min-1) is identified. Our work provides an example for ML-assisted optimization of single-atom coordination environments and illuminates the feasibility of ML in accelerating the development of high-performance catalysts.