Digital Features of Chemical Elements Extracted from Local Geometries in Crystal Structures

Vasylenko, Andrij ORCID: 0000-0002-6933-0628, Antypov, Dmytro ORCID: 0000-0003-1893-7785, Schewe, Sven ORCID: 0000-0002-9093-9518, Daniels, Luke M ORCID: 0000-0002-7077-6125, Claridge, John B ORCID: 0000-0003-4849-6714, Dyer, Matthew Stephen ORCID: 0000-0002-4923-3003 and Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 (2025) Digital Features of Chemical Elements Extracted from Local Geometries in Crystal Structures Digital Discovery, 4 (2). pp. 477-485. ISSN 2635-098X, 2635-098X

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Abstract

Computational modelling of materials using machine learning (ML) and historical data has become integral to materials research across physical sciences. The accuracy of predictions for material properties using computational modelling...

Item Type:	Article
Uncontrolled Keywords:	3403 Macromolecular and Materials Chemistry, 34 Chemical Sciences, Machine Learning and Artificial Intelligence
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	10 Jan 2025 10:23
Last Modified:	16 Jun 2026 17:07
DOI:	10.1039/d4dd00346b
Open Access URL:	https://pubs.rsc.org/en/content/articlelanding/202...
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3189619
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