Interpretable Machine Learning Approach for Photocatalytic Degradation in Mn-Doped Semiconductors Using Multilayer Perceptron and SHAP Analysis

Abstract

This study comprehensively investigates the degradation performance of a Mn-doped Zn2SnO4 photocatalyst based on time-dependent UV-Vis absorption spectra. Before machine learning modelling, the effects of experimental parameters such as UV-Vis measurement wavelength, reaction time, and Mn doping ratio were statistically validated using One-Way Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) methods. To overcome the limitations of linear models in representing complex physical systems, an optimized Multi-Layer Perceptron (MLP) architecture was developed to capture the system's nonlinear dynamics with high accuracy. To validate the model's out-of-sample prediction capability and prevent data leakage potentially arising from spectral data correlation, the Leave-One-Doping-Level-Out (LODLO) cross-validation strategy was applied, during which performance metrics of R-2 = 0.8889 and M S E = 0.00238 were recorded. To make the neural network's decision-making mechanism transparent, a dual-validation explainability framework comprising Shapley Additive Explanations (SHAP) and Permutation Feature Importance analyses was employed. By quantifying the relative contributions of the experimental parameters to the model predictions, this approach revealed that the UV-Vis measurement wavelength was the dominant predictive variable, followed by the Mn doping ratio and reaction time. This study presents a transparent methodology that offers both strong predictive capability and physically grounded data to shed light on the complex interactions in doped semiconductor photocatalysts.