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Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications
Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications
The field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This overview will discuss the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property.
0883-7694
Clements, Rebecca Jane
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Dickman, Joshua Thomas
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Johal, Jay
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Martin, Jennifer Eleanor
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Glover, Joseph
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Day, Graeme M.
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Clements, Rebecca Jane
c92a2d47-ede3-4ce9-bd61-81b8c9681aa6
Dickman, Joshua Thomas
0c21afcd-a554-4a69-ab08-b9e1f2c57ce5
Johal, Jay
4c6e6d6f-f131-48d6-bf72-27154b3f6e76
Martin, Jennifer Eleanor
86423d40-1fac-4d4b-85ba-8fb091de1e3a
Glover, Joseph
27469618-4dd0-44c1-8267-ff955bce66b7
Day, Graeme M.
e3be79ba-ad12-4461-b735-74d5c4355636

Clements, Rebecca Jane, Dickman, Joshua Thomas, Johal, Jay, Martin, Jennifer Eleanor, Glover, Joseph and Day, Graeme M. (2022) Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications. MRS Bulletin. (In Press)

Record type: Article

Abstract

The field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This overview will discuss the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property.

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MRS_review_revised_accepted - Accepted Manuscript
Restricted to Repository staff only until 14 October 2023.
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Accepted/In Press date: 14 October 2022

Identifiers

Local EPrints ID: 471566
URI: http://eprints.soton.ac.uk/id/eprint/471566
ISSN: 0883-7694
PURE UUID: 3ca2a9f4-ea8c-4556-8db8-40caaaa3b498
ORCID for Joseph Glover: ORCID iD orcid.org/0000-0002-5281-6664
ORCID for Graeme M. Day: ORCID iD orcid.org/0000-0001-8396-2771

Catalogue record

Date deposited: 11 Nov 2022 17:41
Last modified: 01 Feb 2023 02:58

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Contributors

Author: Rebecca Jane Clements
Author: Joshua Thomas Dickman
Author: Jay Johal
Author: Jennifer Eleanor Martin
Author: Joseph Glover ORCID iD
Author: Graeme M. Day ORCID iD

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