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A new graph-based order parameter is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system, and is transferable to all structural geometries. Three sets of data are…

Materials Science · Physics 2022-03-22 James Chapman , Nir Goldman , Brandon Wood

Modeling signals as linear combinations of atoms from a dictionary is ubiquitous in modern signal processing. In the finite-dimensional setting, whenever atoms depend nonlinearly upon unknown parameters, the signal model is said to be…

Signal Processing · Electrical Eng. & Systems 2026-05-04 Santos Michelena , Maxime Ferreira Da Costa , José Picheral

This paper presents a set of general strategies for the analysis of structure in amorphous materials and a general approach to assessing the utility of a selected structural description. Measures of structural diversity and utility are…

Soft Condensed Matter · Physics 2020-04-10 Dan Wei , Jie Yang , Min-Qiang Jiang , Lan-Hong Dai , Yun-Jiang Wang , Jeppe Dyre , Ian Douglass , Peter Harrowell

The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not…

Materials Science · Physics 2018-12-26 Ankit Jain , Thomas Bligaard

Characterization of medium-range order in amorphous materials and its relation to short-range order is discussed. A new topological approach is presented here to extract a hierarchical structure of amorphous materials, which is robust…

Soft Condensed Matter · Physics 2015-02-27 Takenobu Nakamura , Yasuaki Hiraoka , Akihiko Hirata , Emerson G. Escolar , Yasumasa Nishiura

We consider an infinitesimal volume where there are many rigid molecules of the same kind, and discuss the description and classification of the local anisotropy in this volume by tensors. First, we examine the symmetry of a rigid molecule,…

Mathematical Physics · Physics 2020-12-18 Jie Xu

The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract…

Materials Science · Physics 2023-08-25 Anthony Onwuli , Ashish V. Hegde , Kevin Nguyen , Keith T. Butler , Aron Walsh

By applying the projector to the filled lattice eigenstates on a specific position, or applying the local electron annihilation operator on the many-body ground state, one can construct a quantum state localized around a specific position…

Mesoscale and Nanoscale Physics · Physics 2025-03-06 Lucas A. Oliveira , Wei Chen

We present a thorough study of the complexity of optical localized modes in two-dimensional disordered photonic crystals. Direct experimental measurements of complexity were made using an interferometric setup that allowed for extraction of…

Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with…

We consider the popular and classical method of alternating projections for finding a point in the intersection of two closed sets. By situating the algorithm in a metric space, equipped only with well-behaved geodesics and angles (in the…

Optimization and Control · Mathematics 2022-06-10 Adrian S. Lewis , Genaro López-Acedo , Adriana Nicolae

Accurate measurement of relative distance and orientation of two nearby quantum particles is discussed. We are in particular interested in a realistic description requiring as little prior knowledge about the system as possible. Thus,…

Quantum Physics · Physics 2015-05-14 Qurrat-ul-Ain Gulfam , Joerg Evers

Incorporating known symmetries in data into machine learning models has consistently improved predictive accuracy, robustness, and generalization. However, achieving exact invariance to specific symmetries typically requires designing…

Machine Learning · Computer Science 2026-03-03 Cindy Y. Zhang , Elif Ertekin , Peter Orbanz , Ryan P. Adams

A central challenge in quantum metrology is identifying optimal measurements that saturate the quantum Cramer-Rao bound under realistic constraints, e.g., local measurements. We show that symmetries of the probe state provide a general…

Quantum Physics · Physics 2026-03-10 Jia-Xuan Liu , Hai-Long Shi , Chunfeng Wu , Sixia Yu

Global quantum sensing enables parameter estimation across arbitrary ranges with a finite number of measurements. Among the various existing formulations, the Bayesian paradigm stands as a flexible approach for optimal protocol design under…

Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…

Materials Science · Physics 2022-06-22 Udaykumar Gajera , Loriano Storchi , Danila Amoroso , Francesco Delodovici , Silvia Picozzi

Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space.…

Materials Science · Physics 2023-07-28 Elena Gazzarrini , Rose K. Cersonsky , Marnik Bercx , Carl S. Adorf , Nicola Marzari

Many tools and techniques measure local structure in materials in contexts ranging from biology to geology. We provide a survey of those tools and metrics that are especially useful for analyzing particulate soft matter. The metrics we…

Soft Condensed Matter · Physics 2026-01-13 Rachael S. Skye , Erin G. Teich

In this article, we study Wasserstein-type metrics and corresponding barycenters for mixtures of a chosen subset of probability measures called atoms hereafter. In particular, this works extends what was proposed by Delon and Desolneux [A…

Optimization and Control · Mathematics 2023-01-20 Geneviève Dusson , Virginie Ehrlacher , Nathalie Nouaime

We present a quantitative measure of physical complexity, based on the amount of information required to build a given physical structure through self-assembly. Our procedure can be adapted to any given geometry, and thus to any given type…

Statistical Mechanics · Physics 2010-09-02 S. E. Ahnert , I. G. Johnston , T. M. A. Fink , J. P. K. Doye , A. A. Louis