Prediction of Bandgap and Key Feature Analysis of Lead-Free Double Perovskite Oxides Based on Deep Learning.

Lead-free double perovskites possess the capabilities of wide bandgap control, excellent photoelectric performance, and environmental friendliness. They are an ideal alternative system for addressing the heavy metal toxicity of lead-based perovskites and promoting their large-scale application. Precise control of their bandgap is key to the green transformation of optoelectronic devices. Bandgap, as a key parameter determining the photoelectric properties of materials, has limitations in traditional experimental determination and DFT calculation methods, such as being time consuming, labour intensive, costly, and difficult to achieve high-throughput screening. Deep learning provides an efficient solution to this problem, but current research has issues such as a single-model architecture and poor interpretability, which cannot effectively support bandgap regulation. This study utilised 2367 valid datasets of lead-free double perovskites sourced from the Materials Project database and relevant literature. Following preprocessing steps, including MinMaxScaler normalisation and Pearson correlation coefficient screening, the dataset was divided into a ratio of 7:1:2. The bandgap prediction capabilities of four models-MLP, deep ensemble learning, PINN, and Transformer-were systematically compared, with feature importance analysed using the SHAP method. The results show that the MLP model performs the best in medium-scale, structured feature prediction. The R