Due to the inherent property of materials, photocatalysis plays a crucial role in diverse applications, such as the degradation of organic pollutants, fuel production, and nitrogen fixation. Perovskites, in particular, are remarkable for their high light absorption rate and energy conversion efficiency within the visible light range, thereby positioning them as promising photocatalysts. Given that specific surface area (SSA) is a critical metric for evaluating photocatalytic performance, accurately and quickly predicting the SSA of perovskites has emerged as a key area of technical research. Therefore, it is essential to explore the relationship between potential materials descriptors and SSA, which can significantly expedite the discovery of photocatalyzed perovskite materials with high SSA. In this study, we proposed a bi-level optimization method that combines descriptors identified strategy and model optimization strategy for predicting the SSA of unknown perovskite candidates, thereby elucidating the relationship between material features and SSA. Initially, at the upper level, the Gradient Boosting Regression combined with Recursive Feature Elimination (RFE-GBR) method was employed to identify crucial features that are closely correlated with the SSA of photocatalyzed perovskites. Subsequently, the Sure Independence Screening.Sparsifying Operator (SISSO) was utilized to develop new descriptors that exhibited strong correlations with SSA. Based on refined descriptors identifiied at the upper level, at the lower level, the optimal model strategy was obtained by Improved Osprey Optimization Algorithm (IOOA) using golden sine strategy and Gradient Boosting Regression (GBR) regression to tune the key parameters of GBR for optimizing SSA. The optimal designing method achieved remarkable predictive performance with R2 (R-squared) score of 0.90 and MAE (Mean Absolute Error) of 3.97 m2/g, along with 5-fold cross-validation. Then this method was applied to predict the SSA of 300 unidentified ABO3 perovskites. Comparison with experimental data revealed that our approach significantly accelerated the development of perovskite materials with superior photocatalytic properties, showing the potential of our model in advancing the field of photocatalysis.
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