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Experimental studies of the glassy slowdown in molecular liquids indicate that the high-temperature activation energy $E_{\infty}$ of glass-forming liquids is directly related to their glass transition temperature $T_{\text{g}}$. To further…

Soft Condensed Matter · Physics 2021-03-02 L. Hecht , R. Horstmann , B. Liebchen , M. Vogel

Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD…

Chemical Physics · Physics 2021-04-15 Lennard Böselt , Moritz Thürlemann , Sereina Riniker

Glasses form the basis of many modern applications and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging…

We explore how the thermodynamic properties and dynamics of a self-interstitial prismatic dislocation loop are affected by microscopic-scale variations in its geometric configuration, an aspect that rarely received attention in literature.…

Materials Science · Physics 2026-05-12 Sho Hayakawa , Sergei L. Dudarev , Max Boleininger

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it…

Soft Condensed Matter · Physics 2019-09-11 Kirk Swanson , Shubhendu Trivedi , Joshua Lequieu , Kyle Swanson , Risi Kondor

Polymer dynamics is analyzed through the lens of linear dimensionality reduction methods, in particular principal (PCA) and time-lagged independent component analysis (tICA). For a polymer undergoing ideal Rouse dynamics, the slow modes…

Soft Condensed Matter · Physics 2025-08-12 Phillip Bement , Joerg Rottler

The question about the existence of a structural glass transition in two dimensions is studied using mode coupling theory (MCT). We determine the explicit d-dependence of the memory functional of mode coupling for one-component systems.…

Soft Condensed Matter · Physics 2009-11-13 M. Bayer , J. Brader , F. Ebert , E. Lange , M. Fuchs , G. Maret , R. Schilling , M. Sperl , J. P. Wittmer

When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called…

Soft Condensed Matter · Physics 2025-06-19 Sai Preetham Sata , Ralf Stannarius , Benjamin Noack , Dmitry Puzyrev

The glass transition of a hard sphere system is investigated within the framework of the density functional theory (DFT). Molecular dynamics (MD) simulations are performed to study dynamical behavior of the system on the one hand and to…

Soft Condensed Matter · Physics 2007-05-23 Kang Kim , Toyonori Munakata

Recent research has focused on designing neural samplers that amortize the process of sampling from unnormalized densities. However, despite significant advancements, they still fall short of the state-of-the-art MCMC approach, Parallel…

Multivariate statistical methods are widely used throughout the sciences, including microscopy, however, their utilisation for analysis of electron backscatter diffraction (EBSD) data has not been adequately explored. The basic aim of most…

Successful computer studies of glass-forming materials need to overcome both the natural tendency to structural ordering and the dramatic increase of relaxation times at low temperatures. We present a comprehensive analysis of eleven…

Statistical Mechanics · Physics 2017-06-09 Andrea Ninarello , Ludovic Berthier , Daniele Coslovich

We develop an algorithm suitable for parallel molecular dynamics simulations in $d$ spatial dimensions and describe its implementation in C++. All routines work in arbitrary $d$; the maximum simulated $d$ is limited only by available…

Soft Condensed Matter · Physics 2022-05-18 Robert S. Hoy , Kevin A. Interiano-Alberto

By combining different ideas, a general and efficient protocol to deal with discontinuous phase transitions at low temperatures is proposed. For small $T$'s, it is possible to derive a generic analytic expression for appropriate order…

Statistical Mechanics · Physics 2015-06-03 C. E. Fiore , M. G. E. da Luz

In this work we use molecular simulations to examine methods of controlling mechanical properties of polymeric glass materials such as elastic moduli, mechanical heterogeneity as well as their glass transition temperature. We study filled…

Soft Condensed Matter · Physics 2016-10-13 George J. Papakonstantopoulos , Juan J. de Pablo

Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and…

Strongly Correlated Electrons · Physics 2023-11-22 F. A. Gómez Albarracín , H. D. Rosales

We propose an iterative proposal to estimate critical points for statistical models based on configurations by combing machine-learning tools. Firstly, phase scenarios and preliminary boundaries of phases are obtained by…

Disordered Systems and Neural Networks · Physics 2019-10-23 X. L. Zhao , L. B. Fu

We study how the thermodynamic properties of the Triangular Plaquette Model (TPM) are influenced by the addition of extra interactions. The thermodynamics of the original TPM is trivial, while its dynamics is glassy, as usual in Kinetically…

Disordered Systems and Neural Networks · Physics 2016-03-23 Silvio Franz , Giacomo Gradenigo , Stefano Spigler

Thermodynamic multi-component solution solidification approach to liquid-to-glass transition is proposed and actual mechanisms underlying vitrification, other than viscous slowdown, are identified. Due to polydisperse aggregation in liquid…

Materials Science · Physics 2022-09-14 Vladimir Belostotsky

Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information…