Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures

The prediction of absorption and emission spectra is a crucial task in chemistry and materials science, traditionally addressed with computational methods, such as density functional theory (DFT). While DFT can deliver with high accuracy, its substantial computational cost and long run times hinder its scalability for large molecular systems. In this study, we present an efficient graph neural network (GNN)-based approach for predicting optical properties. Our method combines molecular graph representations with molecular fingerprints, allowing the model to capture detailed structural and electronic features, as well as solvent effects. This simple yet effective GNN framework achieves <i>R</i> ² values of up to 0.92 for the maximum absorption wavelength and 0.83 for the maximum emission wavelength, significantly improving prediction accuracy while requiring substantially fewer computational resources. Including explicit solvent fingerprints further improves absorption predictions to <i>R</i> ² = 0.96 on the test set, with solvent-specific subsets reaching <i>R</i> ² ≈ 0.99 (e.g., methanol). The results highlight the potential of GNNs to transform spectral prediction workflows and pave the way for accelerated molecular design and materials discovery.