Temporal Decoupling-Based Machine Learning Framework for Precise Efficiency Prediction in Perovskite Solar Cells

Abstract

The rapid and accurate identification of potential high-efficiency design strategies for perovskite solar cells (PSCs) is of paramount importance in advancing their development and commercialization. However, the application of machine learning (ML) algorithms in this field is hindered by unstable PSC data sets (e.g., time-related noise and data imbalance). Here, we introduce a ML framework specifically tailored for temporal decoupling through feature engineering and uncertainty modeling (noise processing techniques) to accurately predict the efficiency of PSCs. In our framework, we utilize one-hot encoding and feature fusion methods to extract features from a shared data set encompassing perovskite material, processing, and PSC architecture. After temporal decoupling, our ML model shows an outstanding precision of 96.88% and a specificity of 99.3% for high-efficiency devices and is successfully applied to predict PSC efficiencies in the period between 2021 and 2023. This temporal decoupling ML framework also reveals hidden relationships between features and efficiency through cross-feature analysis. Our work demonstrates the potential of ML for predicting performance and elucidates the associated mechanisms, accelerating PSC commercialization and reducing costs.