Recognition of near-duplicate periodic patterns by continuous metrics with approximation guarantees

Anosova, Olga D, Widdowson, Daniel E and Kurlin, Vitaliy A ORCID: 0000-0001-5328-5351 (2026) Recognition of near-duplicate periodic patterns by continuous metrics with approximation guarantees PATTERN RECOGNITION, 171. 112108-. ISSN 0031-3203, 1873-5142

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Abstract

This paper rigorously solves the challenging problem of recognizing periodic patterns under rigid motion in Euclidean geometry. The 3-dimensional case is practically important for justifying the novelty of solid crystalline materials (periodic crystals) and for patenting medical drugs in a solid tablet form. Past descriptors based on finite subsets fail when a unit cell of a periodic pattern discontinuously changes under almost any perturbation of atoms, which is inevitable due to noise and atomic vibrations. The major problem is not only to find complete invariants (descriptors with no false negatives and no false positives for all periodic patterns) but to design efficient algorithms for distance metrics on these invariants that should continuously behave under noise. The proposed continuous metrics solve this problem in any Euclidean dimension and are algorithmically approximated with small error factors in times that are explicitly bounded in the size and complexity of a given pattern. The proved Lipschitz continuity allows us to confirm all near-duplicates filtered by simpler invariants in major databases of experimental and simulated crystals. This practical detection of noisy duplicates will stop the artificial generation of ‘new’ materials from slight perturbations of known crystals. Several such duplicates are under investigation by five journals for data integrity.

Item Type:	Article
Additional Information:	Source info: PR-D-24-02181
Uncontrolled Keywords:	Periodic pattern, Isometry invariant, Continuous distance metric
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science & Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	08 Aug 2025 09:23
Last Modified:	16 Jun 2026 20:08
DOI:	10.1016/j.patcog.2025.112108
Open Access URL:	https://doi.org/10.1016/j.patcog.2025.112108
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3193994
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