Predicting Glycerol Electrochemical Oxidation Potentials Using Machine Learning

The electrooxidation of glycerol offers a promising pathway for energy transition and biomass valorization, making it a key area of research. This study employs machine learning (ML) to predict the onset of glycerol electrooxidation and anodic peak potentials, enhancing the understanding of factors influencing these metrics. A dataset derived from 155 research articles includes parameters such as pH, electrolyte type, reference electrode, electrode material, current density, and scan rate. Fourteen ML algorithms were evaluated, with adaptive boosting (AdaBoost) achieving the best performance: root mean square error (RMSE) of 0.117 and coefficient of determination (R2 ) of 0.902 for onset potential and RMSE of 0.122 and R2 of 0.870 for anodic peak potential. Explainable artificial intelligence (XAI) techniques like Shapley additive explanations (SHAP) analysis identified pH, electrolyte type, and electrode properties (e.g., atomic number, electronegativity) as key predictors. Replacing elemental features with atomic properties improved performance and reduced complexity. This work demonstrates the potential of ML to optimize glycerol oxidation and advance alcohol electrooxidation research.