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 for broader open-access community use.