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.