Identification of phases, symmetries and defects through local crystallography. Belianinov, A., He, Q., Kravchenko, M., Jesse, S., Borisevich, A., & Kalinin, S. V. Nature Communications, 6(1):7801, July, 2015. Bandiera_abtest: a Cc_license_type: cc_by Cg_type: Nature Research Journals Number: 1 Primary_atype: Research Publisher: Nature Publishing Group Subject_term: Analytical chemistry;Chemical biology;Condensed-matter physics Subject_term_id: analytical-chemistry;chemical-biology;condensed-matter-physics![Identification of phases, symmetries and defects through local crystallography [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clustering and multivariate algorithms such as principal component analysis explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behaviour.
@article{belianinov_identification_2015,
title = {Identification of phases, symmetries and defects through local crystallography},
volume = {6},
copyright = {2015 The Author(s)},
issn = {2041-1723},
url = {http://www.nature.com/articles/ncomms8801},
doi = {10.1038/ncomms8801},
abstract = {Advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clustering and multivariate algorithms such as principal component analysis explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behaviour.},
language = {en},
number = {1},
urldate = {2021-07-31},
journal = {Nature Communications},
author = {Belianinov, Alex and He, Qian and Kravchenko, Mikhail and Jesse, Stephen and Borisevich, Albina and Kalinin, Sergei V.},
month = jul,
year = {2015},
note = {Bandiera\_abtest: a
Cc\_license\_type: cc\_by
Cg\_type: Nature Research Journals
Number: 1
Primary\_atype: Research
Publisher: Nature Publishing Group
Subject\_term: Analytical chemistry;Chemical biology;Condensed-matter physics
Subject\_term\_id: analytical-chemistry;chemical-biology;condensed-matter-physics},
pages = {7801},
}
Downloads: 0
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