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Many amorphous glassy materials exhibit complex spatio-temporal mechanical response and rheology, characterized by an intermittent stress-strain response and a fluctuating velocity profile. Under quasistatic and athermal deformation…

Soft Condensed Matter · Physics 2010-02-01 Michel Tsamados

We propose a method to predict the value of the external strain where a generic amorphous solid will fail by a plastic response (i.e. an irreversible deformation), solely on the basis of measurements of the nonlinear elastic moduli. While…

Soft Condensed Matter · Physics 2015-05-18 Smarajit Karmakar , Anael Lemaitre , Edan Lerner , Itamar Procaccia

Using positional data from video-microscopy of a two-dimensional colloidal system and from simulations of hard discs we determine the wave-vector-dependent normal mode spring constants in the supercooled fluid and glassy state,…

Soft Condensed Matter · Physics 2012-10-26 Christian L. Klix , Florian Ebert , Fabian Weysser , Matthias Fuchs , Georg Maret , Peter Keim

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications,…

Materials Science · Physics 2016-08-29 Logan Ward , Ankit Agrawal , Alok Choudhary , Christopher Wolverton

The slow flow of amorphous solids exhibits striking heterogeneities: swift localised particle rearrangements take place in the midst of a more or less homogeneously deforming medium. Recently, experimental as well as numerical work has…

Soft Condensed Matter · Physics 2014-03-04 Alexandre Nicolas , Joerg Rottler , Jean-Louis Barrat

Plastic instabilities in amorphous materials are often studied using idealized models of binary mixtures that do not capture accurately molecular interactions and bonding present in real glasses. Here we study atomic scale plastic…

Disordered Systems and Neural Networks · Physics 2020-01-01 Silvia Bonfanti , Roberto Guerra , Chandana Mondal , Itamar Procaccia , Stefano Zapperi

We measure the local yield stress, at the scale of small atomic regions, in a deeply quenched two-dimensional glass model undergoing shear banding in response to athermal quasistatic (AQS) deformation. We find that the occurrence of…

Materials Science · Physics 2020-03-17 Armand Barbot , Matthias Lerbinger , Anaël Lemaître , Damien Vandembroucq , Sylvain Patinet

The effect of periodic shear on strain localization in disordered solids is investigated using molecular dynamics simulations. We consider a binary mixture of one million atoms annealed to a low temperature with different cooling rates and…

Soft Condensed Matter · Physics 2022-09-30 Nikolai V. Priezjev

Goal-conditioned rearrangement of deformable objects (e.g. straightening a rope and folding a cloth) is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a prescribed goal…

Robotics · Computer Science 2023-10-17 Yuhong Deng , Xueqian Wang , Lipeng chen

Amorphous solids under mechanical strains are prone to plastic responses. Recent work showed that in amorphous granular system these plastic events, that are typically quadrupolar in nature, can screen the elastic response. When the density…

Materials Science · Physics 2022-07-20 Avanish Kumar , Michael Moshe , Itamar Procaccia , Murari Singh

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the…

Computational Physics · Physics 2024-02-07 Gregor Maier , Jan Hamaekers , Dominik-Sergio Martilotti , Benedikt Ziebarth

Here we introduce a variation of the trap model of glasses based on softness, a local structural variable identified by machine learning, in supercooled liquids. Softness is a particle-based quantity that reflects the local structural…

Soft Condensed Matter · Physics 2024-03-29 Sean A. Ridout , Indrajit Tah , Andrea J. Liu

In many interesting physical settings, such as the vulcanization of rubber, the introduction of permanent random constraints between the constituents of a homogeneous fluid can cause a phase transition to a random solid state. In this…

Disordered Systems and Neural Networks · Physics 2009-10-31 Paul M. Goldbart

Modern laboratory techniques like ultrafast laser excitation and shock compression can bring matter into highly nonequilibrium states with complex structural transformation, metallization and dissociation dynamics. To understand and model…

Computational Physics · Physics 2022-05-24 Qiyu Zeng , Bo Chen , Xiaoxiang Yu , Shen Zhang , Dongdong Kang , Han Wang , Jiayu Dai

The dynamics of supercooled liquids slow down and become increasingly heterogeneous as they are cooled. Recently, local structural variables identified using machine learning, such as "softness", have emerged as predictors of local…

Soft Condensed Matter · Physics 2024-06-11 Sean A. Ridout , Andrea J. Liu

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

The elementary excitations in metallic glasses (MGs), i.e., $\beta$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those…

Materials Science · Physics 2020-06-25 Qi Wang , Jun Ding , Evan Ma

Establishing reliable and interpretable structure-property relationships in glasses is a longstanding challenge in condensed matter physics. While modern data-driven machine learning techniques have proven highly effective in establishing…

Disordered Systems and Neural Networks · Physics 2026-02-06 Chenyan Wang , Mouyang Cheng , Ji Chen

The plastic deformation of crystalline materials can be understood by considering their structural defects such as disclinations and dislocations. Although glasses are also solids, their structure resembles closely the one of a liquid and…

Disordered Systems and Neural Networks · Physics 2023-06-21 Zhen Wei Wu , Yixiao Chen , Wei-Hua Wang , Walter Kob , Limei Xu

Anelasticity, as an intrinsic property of amorphous solids, plays a significant role in understanding their relaxation and deformation mechanism. However, due to the lack of long-range order in amorphous solids, the structural origin of…

Disordered Systems and Neural Networks · Physics 2023-09-11 Baoshuang Shang