Assessing the structural heterogeneity of supercooled liquids through community inference

Paret, Joris; Jack, Robert L.; Coslovich, Daniele

doi:10.1063/5.0004732

Cited by 70 publications

(75 citation statements)

References 62 publications

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“…Such additions might increase the accuracy, although likely at the cost of the simplicity and speed. Already, a number of UML techniques exist that classify particles based on local structure 9 – 12 , using different definitions of local structures, and different approaches for classification. It will be interesting to see which of these performs best in purely heterogeneous environments like glasses.…”

Section: Discussionmentioning

confidence: 99%

“…Examples include speeding up computationally expensive calculations 5 , accurately distinguishing different crystal phases 6 , 7 , and even developing design rules for structural and material properties 8 . An exciting development is the design of UML algorithms that can autonomously classify particles based on patterns in their local environment 9 – 11 , even in disordered systems 12 . A key strength of these UML approaches is that they can find variations in local structure without any a priori knowledge of what might appear, opening the door to finding new, unanticipated structures.…”

Section: Introductionmentioning

confidence: 99%

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Autonomously revealing hidden local structures in supercooled liquids

et al. 2020

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Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids. To explore this link, much research has been devoted to pinpointing local structures and order parameters that correlate strongly with dynamics. Here we use an unsupervised machine learning algorithm to identify structural heterogeneities in three archetypical glass formers—without using any dynamical information. In each system, the unsupervised machine learning approach autonomously designs a purely structural order parameter within a single snapshot. Comparing the structural order parameter with the dynamics, we find strong correlations with the dynamical heterogeneities. Moreover, the structural characteristics linked to slow particles disappear further away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomously revealing hidden local structures in supercooled liquids

et al. 2020

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“…Note that any other neighbors at a distance r is used in this calculation, irrespective of its species. Similarly, we compute the restricted bond-angle distribution b(θ ) obtained from the angles formed between a central particle of species C belonging to the cores and two of its nearest neighbors, irrespective or their species [61]. Note that the bond-angle distributions are normalized such that the distribution is flat when angles are drawn randomly on a sphere.…”

Section: Local Structure Of Unstable and Stable Coresmentioning

confidence: 99%

“…However, it remains difficult to predict which particles will be associated to the cores. Devising a more general, unsupervised approach to identify structural defects is crucial to predict plastic events in glasses under shear [63] and also dynamic heterogeneities in supercooled liquids [58,61,64].…”

Section: Local Structure Of Unstable and Stable Coresmentioning

confidence: 99%

Spatial structure of unstable normal modes in a glass-forming liquid

et al. 2021

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The phenomenology of glass-forming liquids is often described in terms of their underlying, high-dimensional potential energy surface. In particular, the statistics of stationary points sampled as a function of temperature provides useful insight into the thermodynamics and dynamics of the system. To make contact with the real space physics, however, analysis of the spatial structure of the normal modes is required. In this work, we numerically study the potential energy surface of a glass-forming ternary mixture. Starting from liquid configurations equilibrated over a broad range of temperatures using a swap Monte Carlo method, we locate the nearby stationary points and investigate the spatial architecture and the energetics of the associated unstable modes. Through this spatially-resolved analysis, originally developed to study local minima, we corroborate recent evidence that the nature of the unstable modes changes from delocalized to localized around the mode-coupling temperature. We find that the displacement amplitudes of the delocalized modes have a slowly decaying far field, whereas the localized modes consist of a core with large displacements and a rapidly decaying far field. The fractal dimension of unstable modes around the mobility edge is equal to 1, consistent with the scaling of the participation ratio. Finally, we find that around and below the mode-coupling temperature the unstable modes are localized around structural defects, characterized by a disordered local structure markedly different from the liquid's locally favored structure. These defects are similar to those associated to quasi-localized vibrations in local minima and are good candidates to predict the emergence of localized excitations at low temperature.

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“…Different local structural parameters have been developed over the years. Some examples include the free-volume approach [22], the definition of internal stresses and the symmetry coefficients at the atomic level [23], the concept of flexibility volume [24], the topological cluster classification algorithm [25], the neighbor-distance analysis [26] and the community inference [27].…”

Section: Introductionmentioning

confidence: 99%

Identification of Local Structure in 2-D and 3-D Atomic Systems through Crystallographic Analysis

Ramos¹,

Herranz²,

Foteinopoulou³

et al. 2020

Preprint

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In the present work we revise and extend the Characteristic Crystallographic Element (CCE) norm, an algorithm used to simultaneously detect radial and orientational similarity of computer-generated structures with respect to specific reference crystals and local symmetries. Based on the identification of point group symmetry elements, the CCE descriptor is able to gauge local structure with high precision and finely distinguish between competing morphologies. As test cases we use computer-generated monomeric and polymer systems of spherical particles interacting with the hard-sphere and square-well attractive potentials. We demonstrate that the CCE norm is able to detect and differentiate, between others, among: hexagonal close packed (HCP), face centered cubic (FCC), hexagonal (HEX) and body centered cubic (BCC) crystals as well as non-crystallographic fivefold (FIV) local symmetry in bulk 3-D systems; triangular (TRI), square (SQU) and honeycomb (HON) crystals, as well as pentagonal (PEN) local symmetry in thin films of one-layer thickness (2-D systems). The descriptor is general and can be applied to identify the symmetry elements of any point group for arbitrary atomic or particulate system in two or three dimensions, in the bulk or under confinement.

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Assessing the structural heterogeneity of supercooled liquids through community inference

Cited by 70 publications

References 62 publications

Autonomously revealing hidden local structures in supercooled liquids

Autonomously revealing hidden local structures in supercooled liquids

Spatial structure of unstable normal modes in a glass-forming liquid

Identification of Local Structure in 2-D and 3-D Atomic Systems through Crystallographic Analysis

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