A Physics-Guided Graph Neural Network Framework for Predicting Organic Solar Cell Performance Parameters

Organic solar cells (OSCs) have emerged as a competitive alternative to conventional silicon-based photovoltaics for their inexpensive production, versatility, and reduced energy consumption. However, it is still challenging to accurately assess their performance due to the complex interactions between molecular structure and device-level features. We provide a physics-constrained graph neural network (GNN) architecture for multi-output prediction of key OSC parameters, including power conversion efficiency (PCE), open-circuit voltage, short-circuit current density, and fill factor in this study. To ensure agreement between the anticipated PCE and its physically derived formulation, a physics-guided regularization term is added. Experimental results on a dataset of 5628 samples show that the neural-only GNN achieves strong predictive performance (R2=0.630), outperforming the baseline model random forest (R2=0.537). The proposed physics-constrained GNN maintains comparable accuracy (R2=0.626) while significantly reducing physics violation (from 0.406 to 0.104). These results show that adding physics constraints makes predictions more consistent without lowering accuracy, making it a reliable way to predict OSC performance.