Predicting the band gap of lead-free inorganic double perovskites using modified parallel residual network

The rapid and precise screening of appropriate inorganic perovskite materials poses a formidable challenge in the field of materials science. Traditional methods for material screening are not only time-consuming but also demand a substantial workforce. In this study, we introduce a modified parallel residual network (PRN) for the purpose of predicting the band gap of lead-free inorganic double perovskite materials, utilizing the atomic composition as the input data. The predictive performance of PRN is assessed using root mean square error (RMSE) and Pearson correlation coefficient ( r) as evaluation metrics. The PRN model yields a band gap prediction with an RMSE of 0.402 eV and an r value of 0.962, respectively. Notably, PRN outperforms various alternative models, including random forest regression (RFR), kernel ridge regression (KRR), support vector regression (SVR), extreme gradient boosting regression (XGBR), one-layer residual variant network (OLRN), and three-layer parallel residual variant network (TLPRN), clearly demonstrating its superior predictive accuracy.