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We perform molecular-dynamics simulations of a molecular system in supercooled states for different values of inertia parameters to provide evidence that the long-time dynamics depends only on the equilibrium structure. This observation is…

Soft Condensed Matter · Physics 2009-11-10 S. -H. Chong , F. Sciortino

We use the ``isoconfigurational ensemble'' [Phys. Rev. Lett. {\bf 93}, 135701 (2004)] to analyze both dynamical and structural properties in simulations of a glass forming molecular liquid. We show that spatially correlated clusters of low…

Materials Science · Physics 2009-11-11 Gurpreet S. Matharoo , M. S. Gulam Razul , Peter H. Poole

The anomalous properties of water in the supercooled state are numerous and well-known. Particularly striking are the strong changes in dynamic properties that appear to display divergences at temperatures close to -- but beyond -- the…

Statistical Mechanics · Physics 2007-05-23 Francis W. Starr , Srikanth Sastry , Francesco Sciortino , H. E. Stanley

We propose and numerically implement a local probe of the static self-induced heterogeneity characterizing glass-forming liquids. The method relies on the equilibrium statistics of the overlap between pairs of configurations measured in…

Soft Condensed Matter · Physics 2022-05-23 Benjamin Guiselin , Gilles Tarjus , Ludovic Berthier

Over times shorter than that required for relaxation of enthalpy, a liquid can exhibit striking heterogeneities. The picture of these heterogeneities is complex with transient patches of rigidity, irregular yet persistent, intersected by…

Soft Condensed Matter · Physics 2010-09-30 Peter Harrowell

Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of glasses due to their enormous composition and…

Soft Condensed Matter · Physics 2023-08-23 Kumar Ayush , Pooja Sahu , Sk Musharaf Ali , Tarak K Patra

We numerically study the interplay between heterogeneous dynamics and properties of negatively curved regions of the potential energy surface in a model glassy system. We find that the unstable modes of saddles and quasi-saddles undergo a…

Statistical Mechanics · Physics 2007-05-23 D. Coslovich , G. Pastore

The presence of dynamical heterogeneities, i.e. nanometer-scale regions containing molecules rearranging cooperatively at very different rates compared to the bulk, is increasingly being recognized as crucial in our understanding of the…

Disordered Systems and Neural Networks · Physics 2015-06-25 Horacio E. Castillo , Azita Parsaeian

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 complexity of glasses makes it challenging to explain their dynamics. Machine Learning (ML) has emerged as a promising pathway for understanding glassy dynamics by linking their structural features to rearrangement dynamics. Support…

Soft Condensed Matter · Physics 2025-02-11 Arabind Swain , Sean Alexander Ridout , Ilya Nemenman

Many systems, including polymers and molecular liquids, when adequately cooled and/or compressed, solidify into a disordered solid, i.e., a glass. The~transition is not abrupt, featuring progressive decrease of the microscopic mobility and…

Soft Condensed Matter · Physics 2019-11-19 Francesco Puosi , Antonio Tripodo , Dino Leporini

Glass-forming liquids grow dramatically sluggish upon cooling. This slowdown has long been thought to be accompanied by a growing correlation length. Characteristic dynamical and static length scales, however, have been observed to grow at…

Disordered Systems and Neural Networks · Physics 2016-08-22 Patrick Charbonneau , Ethan Dyer , Jaehoon Lee , Sho Yaida

In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships…

Chemical Physics · Physics 2023-09-01 Martina Crippa , Annalisa Cardellini , Matteo Cioni , Gábor Csányi , Giovanni M. Pavan

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here we use a…

We show that the dynamics between inherent structures in glass forming systems can be understood in purely dynamical terms, without any reference to ``topographic'' features of the potential energy landscape. This ``non-topographic''…

Statistical Mechanics · Physics 2009-11-10 Ludovic Berthier , Juan P. Garrahan

The physics of glasses can be studied from many viewpoints, from material scientists interested in the development of new materials to statistical physicists inventing new theoretical tools to deal with disordered systems. In these lectures…

Statistical Mechanics · Physics 2007-05-23 Ludovic Berthier

The use of probe molecules to extract the local dynamical and structural properties of complex dynamical systems is an age-old technique both in simulations and experiments. A lot of important information which is not immediately accessible…

Soft Condensed Matter · Physics 2021-03-30 Anoop Mutneja , Smarajit Karmakar

Glass-like materials are nonequilibrium systems where the relaxation time may exceed reasonable time scales of observations. In the present paper a dynamic percolation model is introduced in order to explain the principal properties of…

Condensed Matter · Physics 2007-05-23 A. Vazquez , O. Sotolongo-Costa

Hierarchical dynamics in glass-forming systems span multiple timescales, from fast vibrations to slow structural rearrangements, appearing in both supercooled fluids and glassy states. Understanding how these diverse processes interact…

Soft Condensed Matter · Physics 2025-05-28 Wensi Sun , Yanshuang Chen , Wencheng Ji , Yi Zhou , Hua Tong , Ke Chen , Xiaosong Chen , Hajime Tanaka , Peng Tan

From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous…

Machine Learning · Computer Science 2023-08-31 Quan Nguyen , Emma Lejeune