Source-aware materials informatics for biomass-derived carbon electrodes: predicting supercapacitor capacitance from precursor and process descriptors

Abstract

Biomass-derived porous carbons are promising electrode materials for sustainable electrochemical energy storage, yet their capacitance depends on coupled precursor and process variables that are difficult to optimize experimentally. Here, we present a source-aware materials-informatics framework for predicting the specific capacitance of biomass-derived carbon electrodes directly from raw precursor descriptors and processing conditions. A literature-curated dataset comprising elemental composition, proximate analysis, lignocellulosic structure, and activation variables was evaluated with support vector regression, Random Forest, a Deep Neural Network (DNN), Light Gradient Boosting Machine, and Extreme Gradient Boosting (XGBoost), together with an ensemble strategy, across seven feature combinations. XGBoost delivered the strongest test performance, reaching R2=0.948 for the most informative feature block. Beyond model ranking, the study emphasizes validation realism by contrasting conventional random splits with source-aware evaluation, showing that random partitions can substantially overestimate apparent generalization. Across models, current density, activation temperature, holding time, ash content, and volatile matter emerged as the most influential predictors. The resulting workflow provides practical guidance for precursor screening, process-window definition, and data-driven design of biomass-derived supercapacitor electrodes. The guidance applies within the precursor and process regime represented by the training corpus; experimental verification is recommended for excursions beyond it.