2022
DOI: 10.1103/physrevx.12.021001
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Relaxation Dynamics in the Energy Landscape of Glass-Forming Liquids

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Cited by 23 publications
(49 citation statements)
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“…The other line of research to which our work is related highlights the role of elasticity and plasticity, two mechanisms characteristic of the solid phase, in the equilibrium dynamics of super-cooled liquids. Besides the works [11,12] that we already mentioned, there is growing numerical evidence that long-range, anisotropic stress correlations emerge in isotropic quenched liquid [10,[21][22][23][24].…”
mentioning
confidence: 81%
“…The other line of research to which our work is related highlights the role of elasticity and plasticity, two mechanisms characteristic of the solid phase, in the equilibrium dynamics of super-cooled liquids. Besides the works [11,12] that we already mentioned, there is growing numerical evidence that long-range, anisotropic stress correlations emerge in isotropic quenched liquid [10,[21][22][23][24].…”
mentioning
confidence: 81%
“…The swap Monte Carlo algorithm employs unphysical particle moves to accelerate the equilibration of supercooled liquids and can reach equilibrium states down to the experimental glass transition temperature T g or even below. This algorithmic development allowed progress regarding the analysis of structural and thermodynamic properties of liquid states [16][17][18][19][20], as well as characterisation of the glass below T g [21][22][23][24][25][26][27].…”
Section: Introductionmentioning
confidence: 99%
“…Here, we set n = 4 and the constant values c i (i = 0, 2, 4) are chosen so that V (r) = V (r) = V (r) = 0 at the cutoff distance r cutoff = 2.5σ. With these parameters, mean-field replica theory [40,62] yields the dynamical transition temperature T d 0.5940. We show numerical data of the system with N = 1024 particles in all figures, unless the system size is explicitly mentioned.…”
Section: Model and Methodsmentioning
confidence: 99%
“…The mismatch between the isotropic soft-sphere potential and the square box in which the particles reside complicates direct sampling of A ij from the Boltzmann distribution. To deal with this complication, we use a Markov-chain Monte Carlo method with a simple Metropolis algorithm to sample A ij [62]. We perform 200 Monte Carlo sweeps per pair of particles to ensure convergence to the Boltzmann distribution, and take the last configuration of A as the random shift for the chosen particle configuration.…”
Section: Model and Methodsmentioning
confidence: 99%