Simultaneous prediction of atomic structure and stability of nanoclusters in a wide area of compositions

We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close c...

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Bibliographic Details
Published inarXiv.org
Main Authors Lepeshkin, S V, Baturin, V S, Uspenskii, Yu A, Oganov, Artem R
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.12.2018
Subjects
Atomic structure
Clusters
Composition
Evolutionary algorithms
First principles
Global optimization
Nanoclusters
Silicon dioxide
Structural stability
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Summary:We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close compositions. The resulting speedup is up to 50 times compared to current methods. This enables the first-principles studies of multi-component clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented first-principles global optimization of 315 SinOm clusters with n<=15 and m<=20. The obtained map of Si-O cluster stability shows the existence of both expected (SiO2)n and unexpected (e.g. Si4O18) stable ("magic") clusters, which can be important for miscellaneous applications.
ISSN:2331-8422