Metal‐organic framework (MOF)‐derived materials are promising candidates for energy storage applications, particularly as supercapacitors, owing to their high porosity, tunable compositions, and pseudocapacitive behavior. However, performance optimization traditionally relies on resource‐intensive trial‐and‐error methods with limited insight into complex structural transitions. To address this challenge, a machine learning approach integrating Bayesian optimization (BO) to refine synthesis parameters systematically is presented. The importance of each parameter is assessed using correlation matrices, surrogate model structure, and Shapley values analysis in two models: random forest regressor, which achieves low prediction error, and extra trees regressor, which provides better generalization. This strategy efficiently explores the design space and significantly reduces experimental workload. Focusing on Mn‐MIL‐100‐derived MnO/C composites for supercapacitor applications, this approach identifies optimal conditions to increase energy storage performance while quantifying the influence of key process parameters. These findings demonstrate the potential of AI‐driven strategies to accelerate material discovery, enhance process efficiency, and advance the practical applications of MOF‐derived materials.
Gryc et al. (2025) studied this question.