Tailoring Hematite Photoanodes for Improved PEC Performance: The Role of Alcohol Species Revealed by SHAP Analysis

We explore the synergistic effects of TiO₂ underlayers and varied alcohol species in the precursor solutions on the photoelectrochemical (PEC) performance of hematite photoanodes. Utilizing a robust machine learning (ML) framework combined with comprehensive analytical data sets, we systematically investigate how these modifications influence key physical and chemical properties, directly impacting the efficiency of water splitting processes. Our approach employs an ML model that integrates SHapley Additive exPlanations (SHAP) to quantitatively assess the impact of each dominant descriptor selected in the analytical data on the PEC performance, and they were combined with the SHAP values’ dependence on the experimental operations. Specifically, we focus on the type of alcohol (methanol, ethanol, butanol, and 2-ethyl-1-butanol) used in the precursor solutions as the experimental operation, examining their effects on the dominant descriptors selected in the analytical data. The results from the SHAP analysis reveal that different alcohol species significantly alter the physicochemical properties at the hematite/TiO₂ interface and in bulk hematite. These changes are primarily manifested in the modulation of the density of states and resistance to promote the charge carrier transport. For example, ethanol and butanol were found to enhance the electron density of states at the interface, which correlates with higher photocurrent outputs and improved PEC activity. In contrast, methanol showed a less pronounced effect, suggesting a nuanced interaction between the alcohol molecular structure and hematite surface chemistry. These findings not only underscore the importance of tailored precursor solution chemistry for enhancing PEC performance but also highlight the power of ML tools in uncovering the underlying physical and chemical mechanisms that govern the behavior of complex material systems. This study sets a foundational approach where ML can bridge the gap between empirical observations and theoretical understanding, leading to the rational design of energy materials.