Models and Algorithms for the Next Generation of Glass Transition Studies

By Andrea Ninarello, Ludovic Berthier, D. Coslovich. 2017

318 citations recorded by the source corpus.

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 glass-forming models to demonstrate that both challenges can be efficiently tackled using carefully designed models of size polydisperse supercooled liquids together with an efficient Monte Carlo algorithm where translational particle displacements are complemented by swaps of particle pairs. We study a broad range of size polydispersities, using both discrete and continuous mixtures, and we systematically investigate the role of particle softness, attractivity and non-additivity of the interactions. Each system is characterized by its robustness against structural ordering and by the efficiency of the swap Monte Carlo algorithm. We show that the combined optimisation of the potential's softness, polydispersity and non-additivity leads to novel computer models with excellent glass-forming ability. For such models, we achieve over ten orders of magnitude gain in the equilibration timescale using the swap Monte Carlo algorithm, thus paving the way to computational studies of static and thermodynamic properties under experimental conditions. In addition, we provide microscopic insights into the performance of the swap algorithm which should help optimizing models and algorithms even further.

Subfields: Electronic, Optical and Magnetic Materials, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/088307a5521046b2bc06d720fd9a4ebba8a55ede

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electronic, Optical and Magnetic Materials

In Materials Chemistry

Citations in the corpus

Predicting plasticity in disordered solids from structural indicators (2020, unknown): “Recent methodological developments [43,44] now allow very deep supercooling of model polydisperse liquids, to temperatures comparable to and even lower than what can be achieved with laboratory liquids.”
Universality of jamming of nonspherical particles (2018, methodology): “Breathing particles model – The BP model (45) was originally introduced to understand the physics of the Swap Monte Carlo algorithm (46), but here we will focus on its relation with the jamming of ellipsoids.”
Comparing dynamics: deep neural networks versus glassy systems (2018, background): “Indeed, the dynamics of glassy systems was recently shown to be greatly accelerated by adding continuous parameters (Ninarello et al., 2017).”