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Related papers: Ultrastable metallic glasses in silico

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A new Monte Carlo algorithm for 2-dimensional spin glasses is presented. The use of clusters makes possible global updates and leads to a gain in speed of several orders of magnitude. As an example, we study the 2-dimensional +/-J…

Disordered Systems and Neural Networks · Physics 2009-11-07 J. Houdayer

We use a standard Monte-Carlo algorithm to study the slow dynamics of a binary Lennard-Jones glass-forming mixture at low temperature. We find that Monte-Carlo is by far the most efficient way to simulate a stochastic dynamics since…

Statistical Mechanics · Physics 2015-06-25 Ludovic Berthier , Walter Kob

Traditionally, increasing compositional complexity and chemical diversity of high entropy alloy ceramics whilst maintaining a stable single-phase solid solution has been a primary design strategy for the development of new ceramics.…

Materials Science · Physics 2024-03-19 O. V. Pshyk , A. Vasylenko , P. Kuttel , B. Wicher , P. Schweizer , J. Michler , T. E. J. Edwards

An empirical potential that has been widely used to perform molecular dynamics studies on the fracture behavior of FeP metallic glasses is shown to exhibit spinodal decomposition in the composition range commonly studied. The phosphorous…

Materials Science · Physics 2019-01-30 Yezeng He , Peng Yi , Michael L. Falk

In recent years, the possibility of algorithmically preparing ultra-stable glasses (UG), i.e., states that lie very deep in the potential energy landscape, has considerably expanded our understanding of the glassy state. In this work, we…

Soft Condensed Matter · Physics 2024-05-22 Fabio Leoni , John Russo , Francesco Sciortino , Taiki Yanagishima

We report results from Monte Carlo simulations of the two- and three-dimensional gauge glass at low temperature using parallel tempering Monte Carlo. In two dimensions, we find strong evidence for a zero-temperature transition. By means of…

Disordered Systems and Neural Networks · Physics 2007-05-23 Helmut G. Katzgraber

Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust…

Materials Science · Physics 2026-03-18 Hayato Wakai , Atsuto Seko , Isao Tanaka

Crucial to gaining control over crystallisation in multicomponent materials or accurately modelling rheological behaviour of magma flows is to understand the mechanisms by which crystal nuclei form. The microscopic nature of such nuclei,…

Soft Condensed Matter · Physics 2019-08-07 Trond S. Ingebrigtsen , Jeppe C. Dyre , Thomas B. Schrøder , C. Patrick Royall

Using a distinguishable-particle lattice model based on void-induced dynamics, we successfully reproduce the well-known linear relation between heat capacity and temperature at very low temperatures. The heat capacity is dominated by…

Soft Condensed Matter · Physics 2022-12-14 Xin-Yuan Gao , Hai-Yao Deng , Chun-Shing Lee , J. Q. You , Chi-Hang Lam

We simulate crystallization and melting with local Monte Carlo (LMC), event-chain Monte Carlo (ECMC), and with event-driven molecular dynamics (EDMD) in systems with up to one million three-dimensional hard spheres. We illustrate that our…

Statistical Mechanics · Physics 2015-09-02 Masaharu Isobe , Werner Krauth

The correlations of the free-energy landscape of mean-field spin glasses at different temperatures are investigated, concentrating on models with a first order freezing transition. Using a ``potential function'' we follow the metastable…

Disordered Systems and Neural Networks · Physics 2009-10-30 A. Barrat , S. Franz , G. Parisi

Motivated by the concept of geometrical frustration, we introduce a class of statistical mechanics lattice models for the glass transition. Monte Carlo simulations in three dimensions show that they display a dynamical glass transition…

Statistical Mechanics · Physics 2009-11-07 Giulio Biroli , Marc Mezard

There exists a variety of theories of the glass transition and many more numerical models. But because the models need built-in complexity to prevent crystallization, comparisons with theory can be difficult. We study the dynamics of a…

Statistical Mechanics · Physics 2010-04-16 P. Charbonneau , A. Ikeda , J. A. van Meel , K. Miyazaki

Recent work on the zero temperature phases and phase transitions of strongly random electronic system is reviewed. The transition between the spin glass and quantum paramagnet is examined, for both metallic and insulating systems. Insight…

Condensed Matter · Physics 2009-10-28 Subir Sachdev , N. Read

In this work we establish an approach to model miscibility gaps of alloys using statistical physics, lattice dynamics from first-principles calculations. We carefully calculate the entropy to include all processes introducing disorder to…

Materials Science · Physics 2023-02-01 Andrzej P. Kądzielawa , Dominik Legut

In this paper, we introduce a dynamical Monte Carlo algorithm for spin models in which the number of the spins fluctuates from zero to a given number by addition and deletion of spins with a probabilistic rule. Such simulations are realized…

Statistical Mechanics · Physics 2009-10-31 Yukito Iba

We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time…

Soft Condensed Matter · Physics 2024-02-27 Gerhard Jung , Giulio Biroli , Ludovic Berthier

Cycling of a metallic glass between ambient and cryogenic temperatures can induce higher-energy states characteristic of glass formation on faster cooling. This rejuvenation, unexpected because it occurs at small macroscopic strains and…

Disordered Systems and Neural Networks · Physics 2022-09-22 Baoshuang Shang , Weihua Wang , Alan Lindsay Greer , Pengfei Guan

When a liquid is cooled below its melting temperature, if crystallization is avoided, it forms a glass. This phenomenon, called glass transition, is characterized by a marked increase of viscosity, about 14 orders of magnitude, in a narrow…

Disordered Systems and Neural Networks · Physics 2008-08-23 Simone Capaccioli , Giancarlo Ruocco , Francesco Zamponi

Interatomic potentials are key to uncovering microscopic structure-property relationships, essential for multiscale simulations and high-throughput experiments. For metallic glasses, their disordered atomic structure makes the development…