Designing Two-Dimensional Halide Perovskites Based on High-Throughput Calculations and Machine Learning.

The interactions between ions and the low-dimensional halide perovskites are critical to realizing the next-generation energy storage devices such as photorechargeable ion batteries and ion capacitors. In this study, we performed high-throughput calculations and machine-learning analysis for ion adsorption on two-dimensional ABX halide perovskites. The first-principles calculations obtained an initial data set containing adsorption energies of 640 compositionally engineered ion/perovskite systems with diverse ions including Li, Zn, K, Na, Al, Ca, Mg, and F. The machine learning algorithms including k-nearest neighbors (KNN), Kriging, Random Forest, Rpart, SVM, and Xgboost algorithms were compared, and the Xgboost algorithm achieved the best accuracy ( = 0.97, = 0.93) and was selected to predict the virtual design space consisting of 11 976 ion/perovskite systems. The features were then analyzed and ranked according to their Pearson correlations to the output values. In particular, to better understand the features, diverse feature selection methods were employed to comprehensively evaluate the features. The machine-learning-predicted virtual design space was subsequently screened to select stable lead-free ion/perovskite systems with suitable band gaps and halogen mixing features. The present study provides a theoretical foundation to design halide perovskite materials for ion-based energy storage applications such as secondary ion batteries, ion capacitors, and solar-rechargeable batteries.