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 R2 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.
Aljaafreh et al. (Tue,) studied this question.