Rapid screening B-site doping ferroelectric perovskite with high curie temperature for electronic applications by a novel IDBO-RF approach

Abstract

Ferroelectric perovskites with high Curie temperatures (Tc) have emerged as a significant focus in the field of electronic materials in recent years. However, accurately predicting Tc remains a longstanding challenge in the discovery of new functional materials. Therefore, it is crucial to explore the relationship between potential materials descriptors and Tc, which can greatly expedite the identification of ferroelectric perovskite materials with high Tc. In this study, a novel intelligence approach was proposed as a promising solution for identifying Pb[Bx′B1−x′′]O3-type ferroelectric perovskite materials with B-site doping. To achieve this, a comprehensive set of features was utilized to generate synthetic dataset based on available experimental observations from literature and databases. By applying predictive machine learning (ML) models to analyze the synthetic dataset, four key descriptors were identified that exhibited strong correlations with Tc in these materials. First, considering the application of compressed sensing method to reduce high dimensional features, a novel descriptor cos(BCCvd)/(Ec*BCCe) was created and identified, which combined with the first ionization energy (IE1), the lattice energy difference between atoms and their neighboring coordinated atoms (BCCed), and the spin magnetic moment (GSm). These descriptors were found to be closely correlated with Tc of ferroelectric perovskites. Additionally, the Shapley Additive Explanation (SHAP) method was employed to analyze the relationship between Tc and the selected descriptors. Furthermore, based on the selected Random Forest (RF) model with superior performance among several machine learning models tested, an optimization algorithm was proposed combining Improved Dung Beetle Optimizer (IDBO) and RF regression for predicting Tc. Then the designed hybrid IDBO-RF model achieved a mean absolute error (MAE) of 13.95 K in Tc prediction, surpassing traditional methods based on tolerance factor and ionic displacement, which had a MAE of 30.2 K. From a pool of 2745 candidate materials, two potential ferroelectric perovskites with high Tc were successfully screened and identified. This proposed framework will facilitate Tc prediction technology for the discovery of ferroelectric perovskite materials with favorable performance.