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Title: A fingerprint based metric for measuring similarities of crystalline structures

Abstract

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings.

Authors:
; ; ; ;  [1];  [1]; ; ;  [2];  [3];  [4]
  1. Department of Physics, Universität Basel, Klingelbergstr. 82, 4056 Basel (Switzerland)
  2. Institute for Advanced Studies in Basic Sciences, P.O. Box 45195-1159, Zanjan (Iran, Islamic Republic of)
  3. Physics Department, Shahid Beheshti University, G. C., Evin, 19839 Tehran (Iran, Islamic Republic of)
  4. Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208 (United States)
Publication Date:
OSTI Identifier:
22493662
Resource Type:
Journal Article
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 144; Journal Issue: 3; Other Information: (c) 2016 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0021-9606
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; ATOMS; COORDINATES; CRYSTAL STRUCTURE; CRYSTALS; DISTANCE; EXPLORATION; METRICS; POTENTIAL ENERGY; RANDOMNESS; VECTORS

Citation Formats

Zhu, Li, Fuhrer, Tobias, Schaefer, Bastian, Grauzinyte, Migle, Goedecker, Stefan, Amsler, Maximilian, Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, Faraji, Somayeh, Rostami, Samare, Ghasemi, S. Alireza, Sadeghi, Ali, and Wolverton, Chris. A fingerprint based metric for measuring similarities of crystalline structures. United States: N. p., 2016. Web. doi:10.1063/1.4940026.
Zhu, Li, Fuhrer, Tobias, Schaefer, Bastian, Grauzinyte, Migle, Goedecker, Stefan, Amsler, Maximilian, Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, Faraji, Somayeh, Rostami, Samare, Ghasemi, S. Alireza, Sadeghi, Ali, & Wolverton, Chris. A fingerprint based metric for measuring similarities of crystalline structures. United States. doi:10.1063/1.4940026.
Zhu, Li, Fuhrer, Tobias, Schaefer, Bastian, Grauzinyte, Migle, Goedecker, Stefan, Amsler, Maximilian, Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, Faraji, Somayeh, Rostami, Samare, Ghasemi, S. Alireza, Sadeghi, Ali, and Wolverton, Chris. Thu . "A fingerprint based metric for measuring similarities of crystalline structures". United States. doi:10.1063/1.4940026.
@article{osti_22493662,
title = {A fingerprint based metric for measuring similarities of crystalline structures},
author = {Zhu, Li and Fuhrer, Tobias and Schaefer, Bastian and Grauzinyte, Migle and Goedecker, Stefan and Amsler, Maximilian and Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208 and Faraji, Somayeh and Rostami, Samare and Ghasemi, S. Alireza and Sadeghi, Ali and Wolverton, Chris},
abstractNote = {Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings.},
doi = {10.1063/1.4940026},
journal = {Journal of Chemical Physics},
issn = {0021-9606},
number = 3,
volume = 144,
place = {United States},
year = {2016},
month = {1}
}

Works referencing / citing this record:

Insightful classification of crystal structures using deep learning
journal, July 2018