An Informatics Approach for Designing Conducting Polymers

Doping conjugated polymers, which are potential candidates for the next generation of organic electronics, is an effective strategy for manipulating their electrical conductivity. However, selecting a suitable polymer-dopant combination is exceptionally challenging because of the vastness of the chemical, configurational, and morphological spaces one needs to search. In this work, high-performance surrogate models, trained on available experimentally measured data, are developed to predict the p-type electrical conductivity and are used to screen a large candidate hypothetical data set of more than 800 000 polymer-dopant combinations. Promising candidates are identified for synthesis and device fabrication. Additionally, new design guidelines are extracted that verify and extend knowledge on important molecular fragments that correlate to high conductivity. Conductivity prediction models are also deployed at www.polymergenome.org for broader open-access community use.

Automated summary

High-performance surrogate models were developed to predict the p-type electrical conductivity of doped conjugated polymers, leading to the screening of a large dataset of polymer-dopant combinations and the identification of promising candidates for synthesis and device fabrication. New design guidelines were extracted from the study, and conductivity prediction models are now available for broader community use on www.polymergenome.org.