ArticleACS omega2026
A Machine Learning Quest to Design Molecular Graph Fingerprints of Organic Chromophores for Adjusting Photoluminescent Quantum Yields.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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