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

arXiv: https://arxiv.org/abs/2111.00939 · HTML: https://mlsanity.com/paper/2111.00939/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Built on by (most cited first, 5 of 5)

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- [Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches](https://mlsanity.com/paper/2510.06030/) (2025)
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- [A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches](https://mlsanity.com/paper/2603.10992/) (2026)

## Abstract (arXiv)

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 SiO2 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 urlhttps://github.com/mammasmias/IterativeRotationsAssignments.