Multiobjective Fluorescent Molecule Design with a Data-Physics Dual-Driven Generative Framework.

Designing fluorescent small molecules requires simultaneous control over optical responses, brightness, and physicochemical constraints across vast, underexplored chemical spaces. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning predictions, and the prohibitive cost of quantum chemical calculations. Here, we present LUMOS, a data- and physics-driven framework for inverse design of fluorescent molecules. LUMOS couples the generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multiobjective evolutionary algorithms, enabling