Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Predictive
computational methods have the potential to significantly
accelerate the discovery of new materials with targeted properties
by guiding the choice of candidate materials for synthesis. Recently,
a planar pyrrole-based azaphenacene molecule (pyrido­[2,3-b]­pyrido­[3′,2′:4,5]­pyrrolo­[3,2-g]­indole, 1) was synthesized and shown to have promising properties
for charge transport, which relate to stacking of molecules in its
crystal structure. Building on our methods for evaluating small-molecule
organic semiconductors using crystal structure prediction, we have
screened a set of 27 structural isomers of 1 to assess
charge mobility in their predicted crystal structures. Machine-learning
techniques are used to identify structural classes across the landscapes
of all molecules and we find that, despite differences in the arrangement
of hydrogen bond functionality, the predicted crystal structures of
the molecules studied here can be classified into a small number of
packing types. We analyze the predicted property landscapes of the
series of molecules and discuss several metrics that can be used to
rank the molecules as promising semiconductors. The results suggest
several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive
synthetic targets.