Computational screening of bioinspired mixed ionic-electronic conductors

Abstract

In recent years, organic mixed conducting polymers and small molecules have shown great potential in bioelectronics, neuromorphic devices and transient electronics. Current mixed conducting materials are mostly derived from pre-existing semiconductors functionalised with polar ethylene glycol side chains; however, these materials still exhibit limited biocompatibility and degradability. Here, we develop a computational/in silico screening pipeline to investigate the potential of bioinspired building blocks as next-generation materials for organic mixed ionic-electronic conductors (OMIECs). Leveraging sustainable design principles and predictors for electronic charge transport and aggregation/conformational order, we compare two approaches to discover potential new mixed conductors: a computational funnel and a genetic algorithm. We apply and evaluate both approaches against a chemical design space created by matching conjugated fragments from the literature on organic semiconductors, hydrolysable linkers and bioinspired fragments, for a total of almost 25 000 unique combinations. Our study demonstrates that, despite the limited chemical diversity of our dataset, both approaches successfully discover many potential donor–linker–acceptor (D–L–A) systems with promising features, namely: low HOMO–LUMO gap, high inter-ring planarity, and low reorganisation energy. We then down-select a few promising D–L–A systems and symmetrically extend their conjugation to obtain small-molecule prototypes, which show competitive reorganisation energies (as low as 123 meV). We propose that this workflow could be applied to larger datasets and tailored to discover novel chemical motifs for OMIECs and other applications.