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The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light on…

Machine Learning · Computer Science 2025-04-24 Axel Levy , Eric R. Chan , Sara Fridovich-Keil , Frédéric Poitevin , Ellen D. Zhong , Gordon Wetzstein

Structure-property relationships in ordered materials have long been a core principle in materials design. However, the intentional introduction of disorder into materials provides structural flexibility and thus access to material…

Dislocation-density-based crystal plasticity (CP) models are introduced to account for the microstructural changes throughout the deformation process, enabling more quantitative predictions of the deformation process compared to slip-system…

Materials Science · Physics 2025-07-24 Jalal Smiri , Oguz Umut Salman , Ioan R. Ionescu

The importance of accurate simulation of the plastic deformation of ductile metals to the design of structures and components is well-known. Many techniques exist that address the length scales relevant to deformation pro- cesses, including…

Materials Science · Physics 2016-08-16 Reese Jones , Jonathan Zimmerman , Giacomo Po

Defects such as dislocations impact materials properties and their response during external stimuli. Defect engineering has emerged as a possible route to improving the performance of materials over a wide range of applications, including…

Materials Science · Physics 2017-11-17 A. Ulvestad , M. Menickelly , S. M. Wild

We show that a microscopic definition of crystal defect, based on the effective mean single-particle potential energy, makes it possible to detect and visualize various types of local and extended crystal defects and develop an effective…

Materials Science · Physics 2007-05-23 M. Patriarca , M. Robles , K. Kaski

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach…

Image and Video Processing · Electrical Eng. & Systems 2021-07-07 C. K. Groschner , Christina Choi , M. C. Scott

Structural response of crystals to an applied external perturbation is important as a key for understanding microscopic origin of physical properties. Experimental investigation of structural response is a great challenge for modern…

Materials Science · Physics 2016-05-24 Semen Gorfman , Oleg Schmidt , Vladimir Tsirelson , Michael Ziolkowski , Ullrich Pietsch

Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors…

Materials Science · Physics 2021-10-06 Jian-Min Zuo , Renliang Yuan , Yu-Tsun Shao , Haw-Wen Hsiao , Saran Pidaparthy , Yang Hu , Qun Yang , Jiong Zhang

A quantitative evaluation of the influence of sampling on the numerical fractal analysis of experimental profiles is of critical importance. Although this aspect has been widely recognized, a systematic analysis of the sampling influence is…

Statistical Mechanics · Physics 2017-06-22 C. Castelnovo , A. Podestà , P. Piseri , P. Milani

Understanding the processes of perovskite crystallization is essential for improving the properties of organic solar cells. In situ real-time grazing-incidence X-ray diffraction (GIXD) is a key technique for this task, but it produces large…

The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample…

Image and Video Processing · Electrical Eng. & Systems 2022-12-16 Howard Yanxon , James Weng , Hannah Parraga , Wenqian Xu , Uta Ruett , Nicholas Schwarz

This work introduces a simple quantitative model for the Frank--Read source, considered to be one of the most important micro-mechanical mechanisms of dislocation creation in crystalline materials. It has long been known that these sources…

Materials Science · Physics 2025-05-19 Thomas Hudson , Filip Rindler , Joshua Rydell

The problem of heterogeneous nucleation of second-phase in alloys in the vicinity of elastic defects is considered. The defect can be a dislocation line or a crack tip residing in a crystalline solid. We use the Ginzburg-Landau equation to…

Materials Science · Physics 2011-10-07 Christina Bjerkén , Ali R. Massih

We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into…

Materials Science · Physics 2025-10-21 Akira Takahashi , Yu Kumagai , Arata Takamatsu , Fumiyasu Oba

Predicting material properties base on micro structure of materials has long been a challenging problem. Recently many deep learning methods have been developed for material property prediction. In this study, we propose a crystal…

Materials Science · Physics 2022-11-22 Xiangrui Yang

We investigate nonlinear one- and two-dimensional photonic crystals by applying a finite element-iterative method.Numerical results show the essential influence of nonlinear elements embedded into a quarter-wave stack and the sharp photonic…

Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy and biology owing to their low dimensionality and dramatic change of properties compared to the bulk…

The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single crystal…

Computational Physics · Physics 2023-12-27 Gabe Guo , Judah Goldfeder , Ling Lan , Aniv Ray , Albert Hanming Yang , Boyuan Chen , Simon JL Billinge , Hod Lipson

Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and…

Materials Science · Physics 2018-07-19 A. Ziletti , D. Kumar , M. Scheffler , L. M. Ghiringhelli
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