IRA: A shape matching approach for recognition and comparison of generic atomic patterns

TL;DR

The paper introduces IRA, a parameter-less shape matching algorithm for atomic structures that handles permutations, rotations, reflections, and distortions, enabling accurate comparison and recognition of complex atomic patterns.

Contribution

IRA is a novel, versatile shape matching method that does not require atomic assignments beforehand and can handle various atomic arrangements and distortions.

Findings

IRA effectively finds rigid transformations and permutations between structures.

The algorithm outperforms existing shape matching methods in efficiency.

Applications include structural analysis of molecules and amorphous materials.

Abstract

We propose a versatile, parameter-less approach for solving the shape matching problem, specifically in the context of atomic structures when atomic assignments are not known a priori. The algorithm Iteratively suggests Rotated atom-centered reference frames and Assignments (Iterative Rotations and Assignments, IRA). The frame for which a permutationally invariant set-set distance, namely the Hausdorff distance, returns minimal value is chosen as the solution of the matching problem. IRA is able to find rigid rotations, reflections, translations, and permutations between structures with different numbers of atoms, for any atomic arrangement and pattern, periodic or not. When distortions are present between the structures, optimal rotation and translation are found by further applying a standard Singular Value Decomposition-based method. To compute the atomic assignments under the one-to-one assignment constraint, we develop our own algorithm, Constrained Shortest Distance Assignments (CShDA). The overall approach is extensively tested on several structures, including distorted structural fragments. Efficiency of the proposed algorithm is shown as a benchmark comparison against two other shape matching algorithms. We discuss the use of our approach for the identification and comparison of structures and structural fragments through two examples: a replica exchange trajectory of a cyanine molecule, in which we show how our approach could aid the exploration of relevant collective coordinates for clustering the data; and an SiO$_2$ amorphous model, in which we compute distortion scores and compare them with a classical strain-based potential. The source code and benchmark data are available at \url{https://github.com/mammasmias/IterativeRotationsAssignments}.