A fingerprint based metric for measuring similarities of crystalline structures

被引：104

作者：

Zhu, Li ^{[1
]}

Amsler, Maximilian ^{[1
,2
]}

Fuhrer, Tobias ^{[1
]}

Schaefer, Bastian ^{[1
]}

Faraji, Somayeh ^{[3
]}

Rostami, Samare ^{[3
]}

Ghasemi, S. Alireza ^{[3
]}

Sadeghi, Ali ^{[4
]}

Grauzinyte, Migle ^{[1
]}

Wolverton, Chris ^{[2
]}

Goedecker, Stefan ^{[1
]}

机构：

[1] Univ Basel, Dept Phys, Klingelbergstr 82, CH-4056 Basel, Switzerland

[2] Northwestern Univ, Dept Mat Sci & Engn, Evanston, IL 60208 USA

[3] Inst Adv Studies Basic Sci, POB 45195-1159, Zanjan, Iran

[4] Shahid Beheshti Univ, Dept Phys, GC, Tehran 19839, Iran

来源：

JOURNAL OF CHEMICAL PHYSICS | 2016年 / 144卷 / 03期

基金：

瑞士国家科学基金会;

关键词：

NIGGLI REDUCTION; POTENTIALS; PREDICTION; GEOMETRY; DATABASE; SEARCH;

D O I：

10.1063/1.4940026

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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. (C) 2016 AIP Publishing LLC.

引用

页数：9

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