Data-driven machine learning (ML) technologies have become increasingly prevalent in the prediction of the optical properties of fluorescent dyes, especially across diverse solvent environments─a key requirement for the rational design of small solvatochromic systems. Here, we introduce KPGT-Fluor, a novel adaptation of the Knowledge-guided Pretraining of Graph Transformer (KPGT) framework, designed to model solvent-dependent photophysical behavior. Through the integration of solvent molecular descriptors, KPGT-Fluor effectively captures solvent environmental effects that influence optical properties. KPGT-Fluor exhibits strong predictive performance, achieving mean absolute error (MAE) of 10.55 and 12.09 nm for absorption wavelengths (λabs) and emission wavelengths (λem), respectively. For the logarithm of the extinction coefficient (ε) and quantum yield (Φ), the MAE values are 0.104 and 0.081, demonstrating a high accuracy. Compared with the existing models, a comprehensive evaluation across the four key property prediction tasks shows that KPGT-Fluor exhibits a more balanced and competitive overall performance. To further demonstrate the effectiveness of the proposed framework, an external test set containing representative main ring structures was selected. Furthermore, two novel D-π-A molecules were synthesized, and their optical properties in different solvents were experimentally compared with KPGT-Fluor predictions. These results highlight KPGT-Fluor as a powerful tool for predicting and discovering solvatochromic materials.
Lyu et al. (Mon,) studied this question.