Discovering Molecular Insights in Organic Optoelectronics with Knowledge-Informed Interpretable Deep Learning

Deep learning holds significant promise for accelerating molecular screening and materials design. However, the black-box nature of current models limits their ability to generate fundamentally new chemical knowledge and insights. Here, we propose LUMIA (Learning and Understanding Molecular Insights with Artificial Intelligence), an innovative interpretable deep learning framework integrating chemistry-informed contrastive learning and Monte Carlo tree search (MCTS). LUMIA is pretrained on approximately 1.4 million organic molecules, using knowledge-informed augmentations that embed π-conjugation and substituent effects explicitly. This allows it to effectively capture hierarchical molecular representations aligned with chemical intuition. Critically, the explicit integration of chemical knowledge enables LUMIA to achieve state-of-the-art performance across multiple organic optoelectronic property prediction tasks. Leveraging its intrinsic interpretability through MCTS, LUMIA directly uncovers previously unexplored substructure patterns influencing reorganization energy, enabling rational molecular design beyond the training data set. Furthermore, LUMIA reveals novel chemical insights, including synergistic effects of substituent positions in pyrazole derivatives. This study highlights the pivotal role of knowledge embedding in interpretable deep learning, transforming molecular design, and accelerating chemical discovery.