Efficient Adversarial Generation of Thermally Activated Delayed Fluorescence Molecules.

ABSTRACT: Adversarial generative models are becoming an essential tool in molecular design and discovery due to their efficiency in exploring the desired chemical space with the assistance of deep learning. In this article, we introduce an integrated framework by combining the modules of algorithmic synthesis, deep prediction, adversarial generation, and fine screening for the purpose of effective design of the thermally activated delayed fluorescence (TADF) molecules that can be used in the organic light-emitting diode devices. The retrosynthetic rules are employed to algorithmically synthesize the D-A complex based on the empirically defined donor and acceptor moieties, which is followed by the high-throughput labeling and prediction with the deep neural network. The new D-A molecules are subseque

SUBMITTER: Tan Z 

PROVIDER: S-EPMC9161419 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature