Publication date: 5th November 2025
Perovskites are promising new-generation solar cell materials with high photovoltaic conversion efficiencies. The development of lead-free perovskite materials requires the prediction of various material properties and analysis of the factors that affect those properties.
The perovskite structure has a high compositional flexibility, so the composition can be adjusted to suit various purposes, such as improving photoelectric conversion efficiency, removing harmful substances, and improving stability. Especially, many experimental and theoretical studies have been published on tuning the band gap and improving stability by compositional approaches such as implanting Ge into Sn-based perovskites or using mixed halides. [1]
Predicting the physical properties of new compositions typically requires experiments and first-principles calculations. However, these methods face challenges in terms of time and computational cost when comprehensively exploring the large compositional space of perovskite structures.
To overcome these complexities, machine learning (ML) has become increasingly popular in recent years. One of these is Crystal Graph Convolutional Neural Networks (CGCNNs) [2], which is able to incorporate local structural effects and is therefore suitable for a wide range of molecules and crystals. Models with this type of network structure tend to be highly expressive and have high prediction accuracy, but they also have the disadvantage of being less explainable, making it difficult to analyze the prediction results.
In this study, we first predicted the band gap using ML model for a dataset containing double perovskites[3] and evaluated the prediction performance. Classic machine learning models like the SVR were chosen. In addition, we performed an analysis using SHAP (SHapley Additive exPlanations) [4] values to see which features have the greatest impact on the model's predictions. Furthermore, we used ML to create input features to add the perovskite structure characteristics, and confirmed their effect on prediction. The results show that ML features increase the accuracy of the band gap prediction and are important for prediction.
We acknowledge financial support from MEXT as “Program for Promoting Researches on the Supercomputer Fugaku” (Realization of innovative light energy conversion materials utilizing the supercomputer Fugaku, JPMXP1020210317).
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