Organic semiconductors (OSC) derived from π-conjugated molecules and polymers are of broad interest across a range of electronic and optical technologies. How molecular design and processing merge to result in OSC with optimal performance for a given application, however, remains largely a mixture of art and science. In the search for machine-driven OSC design and discovery, we seek to develop and implement modeling and data-science-based approaches that allow for physicochemical understanding of the OSC structure-processing-function paradigm. Here we will discuss recent advances in atomistic-scale modeling and machine learning approaches to showcase how machine-informed understanding can lead to understanding required to move away from Edisonian-based materials development.

Deconstructing the genome of organic semiconductors: Developments towards <i>in silico</i> materials design

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.

Deconstructing the genome of organic semiconductors: Developments towards in silico materials design