可解释且高精度的基于三级树的集成混合模型用于预测光电化学电池中的光电流密度和电极电势：理论支持并由实验数据外部验证

Abstract Photocurrent density (J) and potential with respect to reverse hydrogen electrode (V RHE ) are crucial parameters for evaluating the performance of a PEC system for green hydrogen production. The study aims to create a highly accurate, interpretable, robust and versatile machine learning model for the prediction of J and V RHE supported by theory and external validation. In this study, at first, we developed two binary hybrid models (M3, M4) by using BO optimized two single models (M1, M2) and a dataset of 2593 records followed by the development of two tertiary hybrid models (M5, M6) for J and V RHE prediction. The generalizability of all models was assessed applying external validation (EV) technique using five experimental datasets and three physics-based models (PBM) (P1-3) for J. The SHAP technique was utilized to explain the features contribution in hybrid and single models for J and V RHE prediction. In the prediction of J, M4, M5 and M6 achieved the best ever accuracy in terms of R 2 >0.977 (best ever reported accuracy) and M5, M6 achieved the highest among all with R 2 > 0.999. M5 was found to the best generalized models among all models. Interestingly, P3 was found to be the best correlated PBM with all ML based models. Further, the best ever accuracy in terms of R 2 >0.999 was achieved for V RHE prediction (best ever reported value of R 2 =0.712). In EV of V RHE using five unseen experimental datasets, M6 was the best performing model. In the prediction of J and V RHE , the contribution of each feature for single and hybrid models were established using SHAP technique. Bandgap, electrode area, experimental potential were found within the top six influencing parameters in J prediction. Experimental potential was found within the top three influencing features in V RHE prediction. Further, the success of tertiary models was well explored using multiple statistical techniques.