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Evolutionary Chemical Space Exploration for Functional Materials: Computational Organic Semiconductor Discovery

preprint
submitted on 29.01.2020 and posted on 30.01.2020 by Chi Y. Cheng, Josh E. Campbell, Graeme Day
Computational methods, including crystal structure and property prediction, have the potential to accelerate the materials discovery process by enabling structure prediction and screening of possible molecular building blocks prior to their synthesis. However, the discovery of new functional molecular materials is still limited by the need to identify promising molecules from a vast chemical space. We describe an evolutionary method which explores a user specified region of chemical space to identify promising molecules, which are subsequently evaluated using crystal structure prediction. We demonstrate the methods for the exploration of aza-substituted pentacenes with the aim of finding small molecule organic semiconductors with high charge carrier mobility, where the space of possible substitution patterns is too large to exhaustively search using a high throughput approach. The method efficiently explores this large space, typically requiring calculations on only ca.1% of molecules during a search. The results reveal two promising structural motifs: aza-substituted naphtho[1,2-a]anthracenes with reorganisation energies as low as pentacene and a series of pyridazine-based molecules having both low reorganisation energies and high electron affinities.

Funding

EPSRC EP/L015722/1

ERC ERC-stG- 2012-ANGLE grant no 307358

History

Email Address of Submitting Author

g.m.day@soton.ac.uk

Institution

University of Southampton

Country

United Kingdom

ORCID For Submitting Author

0000-0001-8396-2771

Declaration of Conflict of Interest

no conflict of interest

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