Machine-Learning-Assisted Optimization of a Single-Atom Coordination Environment for Accelerated Fenton Catalysis

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.