A Machine Learning Quest to Design Molecular Graph Fingerprints of Organic Chromophores for Adjusting Photoluminescent Quantum Yields

The design of organic chromophores with a high photoluminescent quantum yield (PLQY) is crucial for various optoelectronic applications. However, the vast chemical space of organic chromophores poses a significant challenge for experimental screening. Here, we report a molecular fingerprinting-based deep learning pipeline to discover organic chromophores with the desired PLQY. We convert 713 organic chromophores into 2048-bit fingerprints and screen them using machine learning (ML) techniques to predict their effect on PLQY. Support vector and gradient boosting regressor models achieve good predictive performance, with <i>R</i> ² values ranging from 0.68 to 0.88. By breaking retrosynthetic analysis, we designed 5200 new organic chromophores with desirable PLQY. Furthermore, we visualize and screen 1840 chromophores using structure-activity landscape analysis. Our work demonstrates the power of molecular fingerprinting and ML in designing new chromophores with desired optical properties, providing a useful strategy for accelerating the discovery of high-performance organic materials.