Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning.

ABSTRACT: The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted

SUBMITTER: Peng SP 

PROVIDER: S-EPMC8396663 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature