Predicting Power Conversion Efficiency in Donor-Acceptor Pairs for Organic Solar Cells Using Machine Learning Ensemble Models

This study explores the application of machine learning (ML) models, specifically tree-based and ensemble models, for accurately predicting the power conversion efficiency (PCE) of organic solar cells (OSCs). The employed ML models were examined on 319 donor-acceptor (D-A) records containing important physical/chemical descriptors of organic solar cells. This research aims to develop suitable D-A material to achieve high-performing OSC under any given material conditions. To achieve this goal, multiple ML-based regression models, including Fine Tree (FT), Medium Tree (MT), Coarse Tree (CT), Bagged Tree (BGT), and Boosted Tree (BST), were studied on the dataset. These employed models were evaluated using standard performance metrics: mean absolute error (MAE), root mean squared error (RMSE), coefficient of correlation (R), and coefficient of determination (R²). Among the models examined, the BST ensemble outperformed the others, achieving an R² of 88.75%, a lowest MAE of 0.522, and an RMSE of 0.725 for validation; it also achieved an R² of 85.26%, a lowest MAE of 0.549, and an RMSE of 0.734 for testing. These results indicate that ensemble methods (BST) offer robust generalization and superior performance in complex PCE predictions. This work demonstrates that ML can serve as a reliable and time-efficient alternative to traditional computational methods for estimating OSC physical/chemical properties. The outcomes are expected to assist researchers in accelerating the discovery of the photovoltaic cell in the laboratory.