The University of Southampton
University of Southampton Institutional Repository

Large–scale computational screening of molecular organic semiconductors using crystal structure prediction

Large–scale computational screening of molecular organic semiconductors using crystal structure prediction
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.
0897-4756
4361-4371
Yang, Jack
86dc817f-a317-4659-a7b0-2e818a028010
Sandip, De
40d27cdd-e7e5-4d83-9634-f522d8ecbe88
Campbell, Joshua
09d87084-e709-457b-8a97-1b12acdd1b56
Li, Sean
78ce4384-d33d-4431-bf7d-00779b3c5d96
Ceriotti, Michele
20b1d46a-df80-485e-83fe-3dd9f1229085
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636
Yang, Jack
86dc817f-a317-4659-a7b0-2e818a028010
Sandip, De
40d27cdd-e7e5-4d83-9634-f522d8ecbe88
Campbell, Joshua
09d87084-e709-457b-8a97-1b12acdd1b56
Li, Sean
78ce4384-d33d-4431-bf7d-00779b3c5d96
Ceriotti, Michele
20b1d46a-df80-485e-83fe-3dd9f1229085
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636

Yang, Jack, Sandip, De, Campbell, Joshua, Li, Sean, Ceriotti, Michele and Day, Graeme M. (2018) Large–scale computational screening of molecular organic semiconductors using crystal structure prediction. Chemistry of Materials, 30 (13), 4361-4371. (doi:10.1021/acs.chemmater.8b01621).

Record type: Article

Abstract

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.

Text
revised2 - Accepted Manuscript
Download (4kB)

More information

e-pub ahead of print date: 18 June 2018

Identifiers

Local EPrints ID: 421884
URI: http://eprints.soton.ac.uk/id/eprint/421884
ISSN: 0897-4756
PURE UUID: 8c0ec1ec-1829-440d-a147-207ee378dd7f
ORCID for Graeme M. Day: ORCID iD orcid.org/0000-0001-8396-2771

Catalogue record

Date deposited: 05 Jul 2018 16:30
Last modified: 15 Sep 2021 04:56

Export record

Altmetrics

Contributors

Author: Jack Yang
Author: De Sandip
Author: Joshua Campbell
Author: Sean Li
Author: Michele Ceriotti
Author: Graeme M. Day ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×