A Perovskite Material Screening and Performance Study Based on Asymmetric Convolutional Blocks

Abstract

This study introduces an innovative method for identifying high-efficiency perovskite materials using an asymmetric convolution block (ACB). Our approach involves preprocessing extensive data on perovskite oxide materials and developing a precise predictive model. This system is designed to accurately predict key properties such as band gap and stability, thereby eliminating the reliance on traditional feature importance filtering. It exhibited outstanding performance, achieving an accuracy of 96.8% and a recall of 0.998 in classification tasks, and a coefficient of determination (R2) value of 0.993 with a mean squared error (MSE) of 0.004 in regression tasks. Notably, DyCoO3 and YVO3 were identified as promising candidates for photovoltaic applications due to their optimal band gaps. This efficient and precise method significantly advances the development of advanced materials for solar cells, providing a robust framework for rapid material screening.