Machine Learning—Guided Design of Biomass-Based Porous Carbon for Aqueous Symmetric Supercapacitors

Biomass-derived porous carbon electrodes have attracted significant attention for high-performance supercapacitor applications due to their sustainability, cost-effectiveness, and tunable porosity. To accelerate the design and evaluation of these materials, it is essential to develop accurate and efficient strategies for optimizing their physicochemical and electrochemical properties. Herein, a machine learning (ML) approach is employed to analyze experimental data from previously reported sources, enabling the prediction of specific capacitance (F g⁻¹) based on various material characteristics and processing conditions. The trained ML model evaluates the influence of factors such as biomass type, electrolyte, activating agent, and key synthesis parameters, including activation and carbonization temperatures and durations, on supercapacitor performance. Despite growing interest, comprehensive studies that correlate these variables with performance metrics remain limited. This work addresses this gap by using ML algorithms to uncover the interrelationships between biomass-derived carbon properties, synthesis conditions, and specific capacitance. Herein, it is demonstrated that an optimal combination of a carbonized honeydew peel to H₃PO₄ ratio of 1:4 and an activation temperature of 500 °C yields a highly porous carbon material. When used in a symmetric device with 1 M H₂SO₄ electrolyte, this material, rich in oxygen and phosphorus species, achieves a high specific capacitance of 611 F g⁻¹ at a current density of 1.3 A g⁻¹. Correlation analysis reveals a strong synergy between surface area and pore volume (correlation coefficient = 0.8473), and the ML-predicted capacitance closely aligns with experimental results. This ML-assisted framework offers valuable insights into the critical physicochemical and electrochemical parameters that govern supercapacitor performance, providing a powerful tool for the rational design of next-generation energy storage materials.