Machine Learning-Driven Prediction and Mechanistic Insight into CO <b> ₂ </b> Adsorption on Biomass-Derived Activated Carbons Using Explainable AI (XAI)

To improve CO2 uptake in Biomass-Derived Activated Carbon (BDAC), this study develops a multiscale hybrid digital twin framework. By integrating microscopic descriptors from Density Functional Theory and Molecular Dynamics (DFT/MD) with experimental data from 63 chemically diverse biomass precursors, a Gaussian Process Regression (GPR) model was developed using the Matérn 5/2 Automatic Relevance Determination (ARD) kernel. The framework achieved high internal training accuracy (R2 = 0.968) and Root Mean Square Error (RMSE = 0.2552), while providing a realistic generalization baseline across heterogeneous precursors with a 5-fold Cross-Validated (CV) R2 of 0.1567 and CV RMSE of 0.283. Explainable Artificial Intelligence (XAI) identified a synergistic mechanism for pore filling, revealing the interaction between ln Brunauer–Emmett–Teller (BET) specific surface area and ln total pore volume (ln SBET × ln Vtotal) as the primary mechanical driver (rank 1). Sensitivity analysis identified a maximum thermal window near 400 °C, with 800 °C identified as the critical sintering threshold where structural breakdown begins. Furthermore, the model validates the nonreliance theory of raw material, demonstrating that the geometric surface properties exert a more dominant influence on performance than the biomass origin. The model was effectively stabilized by Bayesian optimization at a minimum internal training loss of 0.065, providing a scalable, materials information-based scheme for using high-resolution virtual screening to accelerate the circular carbon economy.