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Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

Parsaeifard, Behnam and Tomerini, Daniele and De, Deb Sankar and Goedecker, Stefan. (2020) Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression. Journal of Chemical Physics, 153 (21). p. 214104.

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Abstract

Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.
Faculties and Departments:05 Faculty of Science > Departement Physik > Physik > Physik (Goedecker)
UniBasel Contributors:Goedecker, Stefan
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:AIP Publishing
ISSN:0021-9606
e-ISSN:1089-7690
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
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edoc DOI:
Last Modified:10 Dec 2021 12:20
Deposited On:10 Dec 2021 12:20

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