Higher-order, generically complete, continuous, and polynomial-time invariants of crystalline materials

Abstract This paper is at the interface between applied mathematics and numerical computations in materials science. The still open fundamental problem is to quantify the structural novelty of any solid crystalline material (briefly, a periodic crystal) whose atoms can be considered zero-sized points with or without atomic types. Several high-profile papers in Nature reported materials that turned out to be noisy disguises or near-duplicates of already existing structures. We introduce a new mathematical method to quickly and reliably find nearest neighbors of any newly claimed periodic crystal within available databases. The proposed structural invariants are strictly stronger than past descriptors satisfying the same conditions of polynomial-time computability and continuity under noise. The stability and asymptotic of new algorithms are rigorously analyzed.

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Publication Details

Journal
Computational and Applied Mathematics
Published
2026-10-05
DOI
https://doi.org/10.1007/s40314-026-03905-z
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Higher-order, generically complete, continuous, and polynomial-time invariants of crystalline materials

Vitaliy A. Kurlin, Daniel E. Widdowson
Computational and Applied Mathematics
Machine Learning in Materials Science
article

Higher-order, generically complete, continuous, and polynomial-time invariants of crystalline materials

Vitaliy A. Kurlin, Daniel E. Widdowson
article en

Abstract

Abstract This paper is at the interface between applied mathematics and numerical computations in materials science. The still open fundamental problem is to quantify the structural novelty of any solid crystalline material (briefly, a periodic crystal) whose atoms can be considered zero-sized points with or without atomic types. Several high-profile papers in Nature reported materials that turned out to be noisy disguises or near-duplicates of already existing structures. We introduce a new mathematical method to quickly and reliably find nearest neighbors of any newly claimed periodic crystal within available databases. The proposed structural invariants are strictly stronger than past descriptors satisfying the same conditions of polynomial-time computability and continuity under noise. The stability and asymptotic of new algorithms are rigorously analyzed.

Computational and Applied MathematicsVol. 46(2)
University of Liverpool (GB)
Openalex Percentile: Top 27%
Machine Learning in Materials Science
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Higher-order, generically complete, continuous, and polynomial-time invariants of crystalline materials — Vitaliy A. Kurlin, Daniel E. Widdowson · Computational and Applied Mathematics (2026) | TGRS Research Map | TGRS