Subtraction Descriptors in Machine Learning for Optimizing the Cocatalyst Effect of Cobalt Phosphate on Hematite Photoanodes

Abstract

In the quest for sustainable energy solutions, the optimization of the photoelectrochemical (PEC) performance of hematite photoanodes through cocatalysts represents a promising avenue. This study introduces a novel machine learning approach, leveraging subtraction descriptors, to isolate and quantify the specific effects of cobalt phosphate (Co-Pi) as a cocatalyst on hematite's PEC performance. By integrating data from various analytical techniques, including photoelectrochemical impedance spectroscopy and ultraviolet−visible spectroscopy, with advanced machine learning models, we successfully predicted the PEC performance enhancement attributed to Co-Pi. The Gaussian process regression (GPR) model emerged as the most effective, revealing the critical influence of the interfacial resistance, bulk resistance, and interfacial capacitance on the PEC performance. These findings underscore the potential of cocatalysts in improving charge separation and extending charge carrier lifetimes, thereby boosting the efficiency of photocatalytic reactions. This study not only advances our understanding of the cocatalyst effect in photocatalytic systems but also demonstrates the power of machine learning in modifying complex materials and guiding the development of optimized photocatalytic materials. The implications of this research extend beyond hematite photoanodes, offering a generalizable framework for enhancing the photoelectrochemical properties of a wide range of material modifications such as cocatalyst deposition, doping, and passivation.