The specific surface area (SSA) of ABO3-type perovskite is one of the important properties associated with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (in the range of 1–60 m2 g–1) of perovskite and its features, including chemical compositions and technical parameters. The genetic algorithm–support vector regression method was used to screen the main features for modeling. The correlation coefficient (R) between the predicted and experimental SSAs reached as high as 0.986 for the training data set and 0.935 for leave-one-out cross-validation. ABO3-type perovskites with higher SSA can be screened out using the Online Computation Platform for Materials Data Mining (OCPMDM) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite, which is accessible at .

Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO3-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (E g), Curie temperature (T c), and dielectric loss (tan δ) of ABO3-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, E g, and T c are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO3 ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO3 ferroelectric perovskites with desired multiple properties.

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The rapid discovery of high-performance photocatalysts is one of the challenges in the field of photocatalysis research. Here, a multiobjective stepwise design strategy is developed to accelerate the design of potential ABO 3type perovskites with enhanced photocatalytic activity. The strategy includes model building, stepwise screening, and prediction of the hydrogen production rate (R H 2 ) of candidate perovskites. Based on the strategy, 35 candidate perovskites with a high specific surface area (SSA) (>60 m 2 g −1 ), a suitable bandgap (E g ) (1.4−2.6 eV), and a small crystallite size (CS) (<36 nm) are proposed. The predicted results of R H 2 of the candidates show they have high R H 2 (>6000 μmol h −1 g −1 ). The statistical analysis results reveal that a suitable E g is more likely to acquire when the main elements at the A site are Bi, La, and Pr, and those at the B site are Fe, Ti, and Mn. The perovskites prepared using the sol−gel, polymer complex, combustion method, and lower calcination temperature preferably have a higher SSA and a smaller CS. The relationship between target properties and R H 2 demonstrates that candidate perovskites with E g in the range of 1.9−2.4 eV, CS in the range of 20−32 nm, and a high SSA (>50 m 2 g −1 ) have a higher R H 2 . Furthermore, these models have been developed for online prediction applications.