Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides

Abstract Double perovskite halide materials have been widely used in batteries, light‐emitting diodes, and solar cells. Thus, investigations of the fundamental properties of the double perovskite halide to search for an ideal structure are crucial. In this study, a surrogate model is developed to predict the formation energy, convex hull energy, and bandgap of A2BB′X6 type double perovskite halide structures. The material properties of 13 542 candidate structures are predicted and validated through first‐principles calculations. Without double perovskite halide information during training, the prediction accuracy for the formation energy is obtained as an R‐squared value of 0.770 and Root Mean Square Error (RMSE) of 0.404 eV atom−1. For the convex hull energy, an accuracy of 0.642 is obtained. For the bandgap, R‐squared score of 0.427 and an RMSE of 1.235 eV are achieved. Furthermore, the optimization process confirms that adding only 850 (6%) double perovskite halide structures to the training set increases the R‐squared value to 0.90 for the formation energy. In the bandgap, more data are needed; 3550 data (68.2%) are added to achieve an R‐squared score of 0.9. The current study successfully predicts the fundamental properties of double perovskite halides for the accelerated discovery of ideal structures.