Synthesizable Double Perovskite Oxide Search via Machine Learning and High-Throughput Computational Screening

Abstract

Double perovskite structures have great potential for applications in batteries, lighting devices, and energy-harvesting materials. In this study, the synthesizability of ABB'O-3 double perovskite materials is predicted using machine learning. The machine learning algorithms are validated by performing high-throughput computational screening. First, material properties extracted from the Materials Project database are used as training data to develop models to predict the formation energy and convex hull energy of general inorganic materials. A regression model predicts the formation energy of general inorganic materials with a high accuracy; an R-squared value equal to 0.98 and a root-mean-square error of 0.175 eV atom(-1) are recorded. In addition, a classification accuracy for the convex hull energy of 0.77 is calculated, with an F1-score of 0.771, in a separate model. Both models are employed to estimate the possible synthesizability of 11 763 ABB'O3 structures and their accuracy is further validated by performing first-principles calculations, whose classification accuracy for the convex hull energy reaches an accuracy of 0.646, with an F1-score of 0.733. The constructed surrogate model, as well as the materials database, can guide the discovery of synthesizable double perovskite oxide structures.