Bayesian Exploration of Metal-Organic Framework- Derived Nanocomposites for High-Performance Supercapacitors

Metal-organic framework (MOF)-derived materials are promising candidates for energy storage applications, particularly assupercapacitors, owing to their high porosity, tunable compositions, and pseudocapacitive behavior. However, performance opti-mization traditionally relies on resource-intensive trial-and-error methods with limited insight into complex structural transi-tions. To address this challenge, a machine learning approach integrating Bayesian optimization (BO) to refine synthesisparameters systematically is presented. The importance of each parameter is assessed using correlation matrices, surrogate modelstructure, and Shapley values analysis in two models: random forest regressor, which achieves low prediction error, and extra treesregressor, which provides better generalization. This strategy efficiently explores the design space and significantly reduces exper-imental workload. Focusing on Mn-MIL-100-derived MnO/C composites for supercapacitor applications, this approach identifiesoptimal conditions to increase energy storage performance while quantifying the influence of key process parameters. Thesefindings demonstrate the potential of AI-driven strategies to accelerate material discovery, enhance process efficiency, andadvance the practical applications of MOF-derived materials.