A fingerprint based metric for measuring similarities of crystalline structures

TL;DR

This paper introduces a new fingerprint-based metric for quantifying similarities between crystalline structures, enabling more effective exploration of energy landscapes in materials science.

Contribution

The authors propose a novel crystal fingerprint method that provides a mathematically valid distance measure for comparing atomic structures, improving upon traditional cell vector-based approaches.

Findings

The new metric can distinguish different crystalline structures effectively.

It is computationally easy to calculate and applicable in various energy landscape exploration methods.

The method satisfies the properties of a mathematical metric.

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 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 allow to 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 is an useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms and high-throughput screenings.