Machine-Learning and Atomic-Scale Mechanistic Insights for Designing Gradient Porous MOF-Derived Carbon Electrodes

MOF-derived heteroatom-doped porous carbons hold strong potential for supercapacitor electrodes, yet their optimization is hindered by complex coupling among pore geometry, composition, and electrochemical behavior. To clarify these structure–performance relationships, we establish a predictive design strategy by integrating a data-driven machine-learning (ML) framework with density functional theory. A hierarchical ensemble model combining Gradient Boosting and Gaussian Process Regression achieves high predictive accuracy (test R2 = 0.99) and strong noise tolerance. ML analysis reveals that capacitance enhancement originates from a coordinated micro/mesopore architecture. Micropores of ∼1.2 nm coupled with mesopores of ∼2.8 nm create an optimal regime that balances charge storage and ion-transport kinetics. Guided by this insight, an MOF-derived O and Co codoped gradient pore model is constructed to probe atomic-scale mechanisms in a neutral KCl electrolyte. Charge-density analysis shows that C–O–Co hybridization and oxygen-induced electron-rich sites enhance interfacial polarization and ion–electrode interactions. Molecular dynamics simulations demonstrate that electrostatic confinement in micropores, together with chemisorption-assisted charge transfer in mesopores, generates a continuous adsorption-energy gradient, accelerating ion migration and improving both storage density and transport kinetics. Overall, this work provides an interpretable framework for designing next-generation energy-storage electrodes with optimized pore architectures.