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Ideal symmetry is known to break down under almost any noise. One measure of asymmetry in a periodic crystal is the relative multiplicity Z' of geometrically non-equivalent units. However, Z' discontinuously changes under almost any…

Metric Geometry · Mathematics 2026-02-06 Surya Majumder , Daniel Widdowson , Yury Elkin , Olga Anosova , Andrew I Cooper , Graeme M Day , Vitaliy Kurlin

Functional materials that enable many technological applications in our everyday lives owe their unique properties to defects that are carefully engineered and incorporated into these materials during processing. However, optimizing and…

Materials Science · Physics 2023-03-23 Sokseiha Muy , Conrad Johnston , Nicola Marzari

Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications. Scanning electron nanodiffraction…

The displacement field in highly non uniformly strained crystals is obtained by addition of constraints to an iterative phase retrieval algorithm. These constraints include direct space density uniformity and also constraints to the sign…

Materials Science · Physics 2009-11-11 A. A. Minkevich , M. Gailhanou , J. -S. Micha , B. Charlet , V. Chamard , O. Thomas

In this study, we applied Bayesian inference for extended X-ray absorption fine structure (EXAFS) to select an appropriate basis from among Fourier, wavelet and advanced Fourier bases, and we extracted a radial distribution function (RDF)…

Data Analysis, Statistics and Probability · Physics 2021-05-07 Yasuhiko Igarashi , Fabio Iesari , Hiroyuki Setoyama , Toshihiro Okajima , Hiroyuki Kumazoe , Ichiro Akai , Masato Okada

Colloidal self-assembly -- the spontaneous organization of colloids into ordered structures -- has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the…

Soft Condensed Matter · Physics 2021-06-29 Gabriele Maria Coli , Emanuele Boattini , Laura Filion , Marjolein Dijkstra

We propose a one-dimensional, nonconvex elastic constitutive model with higher gradients that can predict spontaneous fracture at a critical load via a bifurcation analysis. It overcomes the problem of discontinuous deformations without…

Analysis of PDEs · Mathematics 2021-03-17 Phoebus Rosakis , Timothy J. Healey , Ugur Alyanak

Crystal property prediction is a crucial aspect of developing novel materials. However, there are two technical challenges to be addressed for speeding up the investigation of crystals. First, labeling crystal properties is intrinsically…

Machine Learning · Computer Science 2023-06-12 Haomin Yu , Yanru Song , Jilin Hu , Chenjuan Guo , Bin Yang

Precise and accurate determination of crystallographic orientation is crucial for engineering van der Waals heterostructures, where the twist angle between layers controls emergent electronic and optical properties. While Electron…

Materials Science · Physics 2026-03-11 R. Bangari , M. Mosayebi , J. Buchner , J. D. Caldwell , N. Bassim , T. G. Folland

Future HEP experiments present stringent challenges to inorganic scintillators in both fast timing response and radiation tolerance. This paper reports recent progress in developing ultrafast inorganic scintillators with sub-ns decay time…

Instrumentation and Detectors · Physics 2022-03-15 Chen Hu , Liyuan Zhang , Ren-Yuan Zhu

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material's target properties. Here we propose a new class…

Materials Science · Physics 2022-10-04 Sherif Abdulkader Tawfik , Salvy P. Russo

In this paper, we develop a framework for solving inverse deformation problems using the FEniCS Project finite element software. We validate our approach with experimental imaging data acquired from a soft silicone beam under gravity. In…

Machine learning algorithms based on artificial neural networks have proven very useful for a variety of classification problems. Here we apply them to a well-known problem in crystallography, namely the classification of X-ray diffraction…

Disordered Systems and Neural Networks · Physics 2019-06-19 Pascal Marc Vecsei , Kenny Choo , Johan Chang , Titus Neupert

Accurate molecular crystal structure prediction is a fundamental goal in academic and industrial condensed matter research and polymorphism is arguably the biggest obstacle on the way. We tackle this challenge in the difficult case of the…

Materials Science · Physics 2016-05-04 Cong Huy Pham , Emine Kucukbenli , Stefano de Gironcoli

Established x-ray diffraction methods allow for high-resolution structure determination of crystals, crystallized protein structures or even single molecules. While these techniques rely on coherent scattering, incoherent processes like…

Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often…

Machine Learning · Computer Science 2023-11-30 Marko Petković , Pablo Romero-Marimon , Vlado Menkovski , Sofia Calero

Crystal structure design is important for the discovery of new highly functional materials because crystal structure strongly influences material properties. Crystal structures are composed of space-filling polyhedra, which affect material…

Materials Science · Physics 2024-02-06 Tomoyasu Yokoyama , Kazuhide Ichikawa , Hisashi Naito

In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (e.g. fission reactors, fusion devices, space applications, etc), design, prediction and control of advanced materials…

Amorphous, glass, and glass-ceramic materials practically always include a significant number (more than eight) of crystalline phases, with the contents of the latter ranging from a few wt.% to several hundredths or tenths of wt.%. The…

A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra. This method…

Materials Science · Physics 2023-05-17 Zhu Liang , Matthew R. Carbone , Wei Chen , Fanchen Meng , Eli Stavitski , Deyu Lu , Mark S. Hybertsen , Xiaohui Qu
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