Literature DB >> 31330726

Anisotropic structural predictor in glassy materials.

Zohar Schwartzman-Nowik1, Edan Lerner2, Eran Bouchbinder1.   

Abstract

There is growing evidence that relaxation in glassy materials, both spontaneous and externally driven, is mediated by localized soft spots. Recent progress made it possible to identify the soft spots inside glassy structures and to quantify their degree of softness. These softness measures, however, are typically scalars, not taking into account the tensorial, anisotropic nature of soft spots, which implies orientation-dependent coupling to external deformation. Here, we derive from first principles the linear response coupling between the local heat capacity of glasses, previously shown to provide a measure of glassy softness, and external deformation in different directions. We first show that this linear response quantity follows an anomalous, fat-tailed distribution related to the universal ω^{4} density of states of quasilocalized, nonphononic excitations in glasses. We then construct a structural predictor as the product of the local heat capacity and its linear response to external deformation, and show that it offers an enhanced predictability of plastic rearrangements under deformation in different directions, compared to the purely scalar predictor.

Entities:  

Year:  2019        PMID: 31330726     DOI: 10.1103/PhysRevE.99.060601

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  3 in total

1.  Pinching a glass reveals key properties of its soft spots.

Authors:  Corrado Rainone; Eran Bouchbinder; Edan Lerner
Journal:  Proc Natl Acad Sci U S A       Date:  2020-02-24       Impact factor: 11.205

2.  Predicting the failure of two-dimensional silica glasses.

Authors:  Francesc Font-Clos; Marco Zanchi; Stefan Hiemer; Silvia Bonfanti; Roberto Guerra; Michael Zaiser; Stefano Zapperi
Journal:  Nat Commun       Date:  2022-05-20       Impact factor: 17.694

3.  Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning.

Authors:  Zhao Fan; Evan Ma
Journal:  Nat Commun       Date:  2021-03-08       Impact factor: 14.919

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.