Generative Deep Learning-Based Efficient Design of Organic Molecules with Tailored Properties.

ABSTRACT: 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 (DB_exp) 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 co

SUBMITTER: Han M 

PROVIDER: S-EPMC11869130 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature