High-Efficiency Non-Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry.

ABSTRACT: Y6 and its derivatives have greatly improved the power conversion efficiency (PCE) of organic photovoltaics (OPVs). Further developing high-performance Y6 derivative acceptor materials through the relationship between the chemical structures and properties of these materials will help accelerate the development of OPV. Here, machine learning and quantum chemistry are used to understand the structure-property relationships and develop new OPV acceptor materials. By encoding the molecules with an improved one-hot code, the trained machine learning model shows good predictive performance, and 22 new acceptors with predicted PCE values greater than 17% within the virtual chemical space are screened out. Trends associated with the discovered high-performing molecules suggest that Y6 derivatives

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC8867193 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature