Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming

Traditional trial-and-error methods for optimizing catalyst synthesis are time-consuming and costly, exploring only a small fraction of the vast combinatorial space. Machine learning (ML) offers a promising alternative but still has the limitation of relying on well-selected initial datasets, which the recent development of active learning (AL) could be addressed. Here, we novelly integrate an AL-derived algorithm, the adaptive learning genetic algorithm (ALGA), into experimental workflows to optimize the synthesis of Fenton-like single-atom catalysts (SACs). Our results show that the closed-loop ALGA framework effectively learns from limited and sparse datasets, greatly reducing the research cycle compared to traditional ML and AL frameworks. By iteratively retaining better-performing genetic information and proactively expanding the search space through mutation and crossover, ALGA identifies the highest-performing Fenton-like Cu SACs with less than 90 experiments. The maximum phenol degradation rate k-value (0.147 min^-1) achieved within the ALGA framework is approximately three times higher than that of the initial dataset and surpasses the reported best Fenton-like Cu SACs. Our successful implementation of ALGA signifies an advancement in SACs synthesis assisted by the AL-derived algorithm, offering a guiding methodology for the exploration of other functional materials.