Data-Driven Machine Learning Strategy for Designing Metal-Ion-Doped γ-BiMoO Photocatalysts to Enhance Degradation Performance.

Doped semiconductors are often used to improve photocatalytic efficiency and address the challenges of easy recombination of electron-hole pairs and poor photoluminescence. However, the reproducibility and complexity of experimental studies result in time-consuming and less cost-effective studies, and it is difficult to gain insights into the intrinsic properties of doped photocatalysts to control their performance. Introducing a machine learning approach, we constructed a photocatalytic model of transition-metal- and rare earth metal-ion-doped γ-BiMoO. We selected 18 factors of preparation conditions and dopant ion properties, and constructed 806 data sets through literature collection for correlation analysis, paving the way for a more efficient and cost-effective research process. The results of our study are promising. The trained and improved XGboost model demonstrated high resistance to the variability caused by data segmentation, with a cross-validated model showing a coefficient of determination of 0.942. Through the combination of characteristic importance and Shapley additive explanation analysis, the importance and correlation trends of preparation conditions and dopant ion properties are obtained, especially the positive correlation trend of excitation time and preparation time and the negative correlation trend of atomic mass and bandwidth. Model prediction and experimental validation are used to demonstrate the effectiveness and behavioral prediction ability, and the Zn and Cd elements are successfully predicted for doping modification means. This study contributes to the modification and preparation of γ-BiMoO materials and provides a solid foundation for the efficient design of photocatalysts.