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We introduce a novel machine learning computational framework that aims to compute the material toughness, after subjected to a short training process on a limited meso-scale experimental dataset. The three part computational framework…

Materials Science · Physics 2021-08-31 Stylianos Tsopanidis , Shmuel Osovski

The imprint effect, a significant reliability challenge in ferroelectric memories, manifests as a shift in the coercive field during retention and endurance tests, ultimately degrading the usable memory window. \rv{While traditional models…

Materials Science · Physics 2025-12-17 Huanhuan Tian , Jianguo Yang , Ming Liu

Functional connectivity estimates are highly sensitive to analysis choices and can be dominated by noise when the number of sampled time points is small relative to network dimensionality. This issue is particularly acute in fMRI, where…

Disordered Systems and Neural Networks · Physics 2026-02-10 Izaro Fernandez-Iriondo , Antonio Jimenez-Marin , Jesus Cortes , Pablo Villegas

We present a multiscale modelling framework that integrates density functional theory (DFT) with a phase-field model (PFM) to explore the intricate dynamics of grain growth in nanocrystalline {\alpha}-Fe single-phase alloy in the presence…

Materials Science · Physics 2024-06-13 Sandip Guin , Albert Linda , Yu-Chieh Lo , Somanth Bhowmick , Rajdip Mukherjee

We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained Nd$_2$Fe$_{14}$B permanent magnets. The embedded Stoner-Wohlfarth method…

The ability to image materials at the microscale from long-wavelength wave data is a major challenge to the geophysical, engineering and medical fields. Here, we present a framework to constrain microstructure geometry and properties from…

Geophysics · Physics 2021-04-19 Wouter Klessens , Ivan Vasconcelos , Yang Jiao

With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in…

Understanding the evolution of dislocation structures during plastic deformation is critical for predicting the mechanical performance of metallic materials. In this work, we applied in situ scanning electron microscopy/electron backscatter…

Materials Science · Physics 2026-03-24 Mikhail Lebyodkin , Maxim Gussev , Jamieson Brechtl , Tatiana Lebedkina

Nonlinear screening of electric depolarization fields, generated by a stripe domain structure in a ferroelectric grain of a polycrystalline material, is studied within a semiconductor model of ferroelectrics. It is shown that the maximum…

Materials Science · Physics 2021-01-14 Yuri A. Genenko , Ofer Hirsch , Paul Erhart

Dielectric response as a function of frequency for high-permittivity dielectric and ferroelectric materials is discussed emphasizing the dynamic behaviour in the microwave and infrared range. After introducing the usual modelling of the…

Materials Science · Physics 2008-12-18 Jan Petzelt , Stanislav Kamba

The internal energy associated with the defect microstructure of strongly deformed crystals provides an important driving force for grain boundary motion during recrystallization. Typical dislocation microstructures are strongly…

Materials Science · Physics 2026-01-13 Yufan Zhang , Michael Zaiser

Science-driven design of future thermoelectric materials requires a deep understanding of the fundamental relationships between microstructure and transport properties. Grain boundaries in polycrystalline materials influence the…

Polycrystalline materials have numerous applications due to their unique properties, which are often determined by the grain boundaries. Hence, quantitative characterization of grain as well as interface orientation is essential to optimize…

Nanostructures have the immense potential to supplant the traditional metallic structure as they show enhanced mechanical properties through strain hardening. In this paper, the effect of grain size on the hardening mechanism of Al-Cu…

Materials Science · Physics 2017-12-27 Satyajit Mojumder , Tawfiqur Rakib , Mohammad Motalab , Dibakar Datta

In recent years, the implementation of thin-film Ta has led to improved coherence times in superconducting circuits. Efforts to further optimize this materials set have become a focus of the subfield of materials for superconducting quantum…

With the development of steel materials, metallographic analysis has become increasingly important. Unfortunately, grain size analysis is a manual process that requires experts to evaluate metallographic photographs, which is unreliable and…

Computer Vision and Pattern Recognition · Computer Science 2026-02-12 Fang Gao , Xuetao Li , Jiabao Wang , Shengheng Ma , Jun Yu

Field-scale properties of fractured rocks play crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion…

Geophysics · Physics 2022-01-05 Zitong Zhou , Delphine Roubinet , Daniel M. Tartakovsky

Additively manufactured (AM) alloys have heterogeneous microstructures with broad grain size distributions and highly anisotropic and/or non-convex grain shapes. AM components can have complex geometries and porosity which may affect the…

Applied Physics · Physics 2026-05-29 Vivian Tong , Hannah Zhang , Jacopo Del Gaudio , Ken Mingard , Ali Gholinia

We performed density functional theory (DFT) calculations on epitaxially strained hafnia. We demonstrate the stabilization of the ferroelectric ($Pca2_{1}$) phase from the antiferroelectric ($Pbcn$) in bulk hafnia in the presence of…

Materials Science · Physics 2023-02-07 Aldo Raeliarijaona , R. E. Cohen

Systematic microstructure design requires reliable thermodynamic descriptions of each and all microstructure elements. While such descriptions are well established for most bulk phases, thermodynamic assessment of crystal defects is…

Materials Science · Physics 2021-07-02 Reza Darvishi Kamachali