Enhancing PCE Prediction for Organic Solar Cells through the Integration of Supervised and Unsupervised Learning

Abstract

Machine learning (ML) has significantly advanced solar cell research, particularly in material optimization and discovery. However, many studies rely on supervised learning models that assume consistent predictive trends across materials, potentially overlooking complex correlations affecting power conversion efficiency (PCE). Unsupervised clustering techniques offer an alternative by uncovering hidden patterns in material properties, yet their application in organic solar cell (OSC) research remains limited. This study addresses this gap by integrating clustering techniques with supervised learning to enhance PCE predictions in OSCs. The research employed K-means, DBSCAN, and hierarchical clustering to categorize OSCs based on molecular descriptors, then incorporated cluster labels as additional features in supervised models including Linear Regression, Random Forest, XGBoost, and Support Vector Regressor. Despite weak inherent cluster structure indicated by clusterability tests, the integration of cluster labels consistently improved predictive performance across all configurations. XGBoost paired with hierarchical clustering achieved the most substantial enhancement, with R² reaching 0.9640 and MAE reducing from 0.2917 to 0.2859. The findings demonstrate that (1) unsupervised learning can identify meaningful structural patterns in OSC datasets, and (2) incorporating cluster labels as engineered features improves PCE prediction accuracy compared to traditional supervised approaches alone. Importantly, even statistically weak clusters provided valuable predictive signals, contributing to enhanced model performance and supporting accelerated discovery of high-efficiency OSC materials