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

Abstract 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.