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Evolutionary chemical space exploration for functional materials: Computational organic semiconductor discovery

Evolutionary chemical space exploration for functional materials: Computational organic semiconductor discovery
Evolutionary chemical space exploration for functional materials: Computational organic semiconductor discovery
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 ∼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.
1478-6524
4922-4933
Cheng, Chi Yang
62f904b4-0308-4a91-8412-17ce69771f06
Campbell, Joshua, Edward
09d87084-e709-457b-8a97-1b12acdd1b56
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636
Cheng, Chi Yang
62f904b4-0308-4a91-8412-17ce69771f06
Campbell, Joshua, Edward
09d87084-e709-457b-8a97-1b12acdd1b56
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636

Cheng, Chi Yang, Campbell, Joshua, Edward and Day, Graeme M. (2020) Evolutionary chemical space exploration for functional materials: Computational organic semiconductor discovery. Chemical Science, 11 (19), 4922-4933. (doi:10.1039/D0SC00554A).

Record type: Article

Abstract

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 ∼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.

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Accepted/In Press date: 21 April 2020
e-pub ahead of print date: 22 April 2020

Identifiers

Local EPrints ID: 439677
URI: http://eprints.soton.ac.uk/id/eprint/439677
ISSN: 1478-6524
PURE UUID: 713198bf-64d3-47e8-b354-6d824b05396f
ORCID for Graeme M. Day: ORCID iD orcid.org/0000-0001-8396-2771

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Date deposited: 29 Apr 2020 16:31
Last modified: 15 Sep 2021 01:59

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Contributors

Author: Chi Yang Cheng
Author: Joshua, Edward Campbell
Author: Graeme M. Day ORCID iD

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