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Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
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Zhang, Liwei, Onat, Berk, Dusson, Geneviève, McSloy, Adam, Anand, G., Maurer, Reinhard J., Ortner, Christoph and Kermode, James R. (2022) Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models. npj Computational Materials, 8 (1). 158. doi:10.1038/s41524-022-00843-2 ISSN 2057-3960.
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Official URL: http://dx.doi.org/10.1038/s41524-022-00843-2
Abstract
We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments. The scheme goes beyond conventional tight binding descriptions as it represents the ab initio model to full order, rather than in two-centre or three-centre approximations. We achieve this by introducing an extension to the atomic cluster expansion (ACE) descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group. The approach produces analytical linear models for the Hamiltonian and overlap matrices. Through an application to aluminium, we demonstrate that it is possible to train models from a handful of structures computed with density functional theory, and apply them to produce accurate predictions for the electronic structure. The model generalises well and is able to predict defects accurately from only bulk training data.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QC Physics T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Chemistry > Computational and Theoretical Chemistry Centre |
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Library of Congress Subject Headings (LCSH): | Materials, Materials -- Computer simulation, Materials -- Mathematical models, Microclusters, Density functionals | |||||||||||||||
Journal or Publication Title: | npj Computational Materials | |||||||||||||||
Publisher: | Nature Publishing Group | |||||||||||||||
ISSN: | 2057-3960 | |||||||||||||||
Official Date: | 22 July 2022 | |||||||||||||||
Dates: |
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Volume: | 8 | |||||||||||||||
Number: | 1 | |||||||||||||||
Article Number: | 158 | |||||||||||||||
DOI: | 10.1038/s41524-022-00843-2 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 22 July 2022 | |||||||||||||||
Date of first compliant Open Access: | 22 July 2022 | |||||||||||||||
RIOXX Funder/Project Grant: |
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