Minhi Han, Joonyoung F. Joung, Minseok Jeong, Dong Hoon Choi, Sungnam Park
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Generative Deep Learning-Based Efficient Design of Organic Molecules with Tailored Properties
Innovative approaches to design molecules with tailored properties are required in various research areas. Deep learning methods can accelerate the discovery of new materials by leveraging molecular structure–property relationships. In this study, we successfully developed a generative deep learning (Gen-DL) model that was trained on a large experimental database (DBexp) including 71,424 molecule/solvent pairs and was able to design molecules with target properties in various solvents. The Gen-DL model can generate molecules with specified optical properties, such as electronic absorption/emission peak position and bandwidth, extinction coefficient, photoluminescence (PL) quantum yield, and PL lifetime. The Gen-DL model was shown to leverage the essential design principles of conjugation effects, Stokes shifts, and solvent effects when it generated molecules with target optical properties. Additionally, the Gen-DL model was demonstrated to generate practically useful molecules developed for real-world applications. Accordingly, the Gen-DL model can be a promising tool for the discovery and design of novel molecules with tailored properties in various research areas, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic photodiodes (OPDs), bioimaging dyes, and so on.
期刊介绍:
ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.