Revealing key structural features hidden in liquids and glasses

Tanaka, Hajime; Tong, Hua; Shi, Rui; Russo, John

doi:10.1038/s42254-019-0053-3

Cited by 204 publications

(192 citation statements)

References 189 publications

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“…We use ‘hard-sphere-like’ to categorise glass formers whose structure ordering is also dominated by the entropy, or the packing effect, which can be captured by our structural order parameter. We stress that many model glass formers with simple isotropic interactions, e.g., those studied in the present work, as well as most of the previous numerical simulation studies, fall into this category 27 . In the absence of obvious density inhomogeneity beyond the particle size, which is the case for these glass formers, the energy term does not play a role in the selection of locally favoured stable structures 23 , although it may take effect as a global constraint as discussed above.…”

Section: Discussionmentioning

confidence: 70%

“…Such a perfect reference is automatically determined for each particle taking into account the particle sizes, the number and arrangement of neighbours, which does not require a prior knowledge of what kind local structure or symmetry would be preferred. Therefore, the advantage of our order parameters lies in their ability to detect exotic amorphous order in an order agnostic manner, which distinguishes them from the common bond orientational order parameters 25,27 .…”

Section: Methodsmentioning

confidence: 99%

“…For instance, icosahedral 24,25 , crystal-like orders 14–16,26,27 , or low-energy topological clusters 28,29 are identified and suggested as the origin of slow dynamics in different glass-forming liquids, but unfortunately with varying degrees of success 30 . This situation may arise from the system-dependent nature of locally low free-energy configurations 16,27 . Soft vibrational modes are also found to show a clear correlation with relaxations in supercooled liquids 31,32 , but one may still wonder what the geometric features underlying the soft structures are.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structural order as a genuine control parameter of dynamics in simple glass formers

Tong

Tanaka

2019

Nat Commun

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Glass transition is characterised by drastic dynamical slowing down upon cooling, accompanied by growing spatial heterogeneity. Its rationalisation by subtle changes in the liquid structure has been long debated but remains elusive, due to intrinsic difficulty in detecting the underlying complex structural ordering. Here we report that structural order parameter characterising local packing capability can well describe the glassy dynamics not only macroscopically but also microscopically, no matter whether it is driven by temperature or density. A Vogel-Fulcher-Tammann (VFT)-like relation is universally identified between the structural relaxation time and the order parameter for supercooled liquids with isotropic interactions. More importantly, we find such an intriguing VFT-like relation to be statistically valid even at a particle level, between spatially coarse-grained structural order and microscopic particle-level dynamics. Such a unified description of glassy dynamics based solely on structural order is expected to contribute to the ultimate understanding of the long-standing glass-transition problem.

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Section: Discussionmentioning

confidence: 70%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural order as a genuine control parameter of dynamics in simple glass formers

Tong

Tanaka

2019

Nat Commun

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“…The conundrum: close to the glass transition, the dynamics slow down dramatically and become heterogeneous 1 , 2 while the structure appears largely unperturbed. Largely unperturbed, however, is not the same as unperturbed, and many studies have attempted to identify slow local structures by exploiting dynamical information 3 , 4 . Unsupervised machine learning (UML) techniques may provide a novel way forward for shedding light on this problem.…”

Section: Introductionmentioning

confidence: 99%

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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“…However, as shown in our previous works (14,17), many factors, such as thermal fluctuations and spatial mixing of different polymorphs, can cause serious distortions of the underlying local structural orders, preventing their proper identifications. Such distortion is a common source of difficulties in local structural identifications for any structure analysis methods (44), including wave-number space analysis (45), common neighbor analysis (46,47), Voronoi index analysis (48)(49)(50), and bond-orientational order approach (51,52).…”

Section: Introductionmentioning

confidence: 99%

Revealing roles of competing local structural orderings in crystallization of polymorphic systems

Chen

Tanaka

et al. 2020

Sci. Adv.

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Most systems have more than two stable crystalline states in the phase diagram, which is known as polymorphism. Crystallization in such a system is often under strong influence of competing orderings linked to those crystals. However, how such competition affects crystal nucleation and ordering toward the final crystalline state is largely unknown. This is primarily because the competition takes place locally and thus is masked by large positional fluctuations. We develop a unique method to correctly identify local symmetries by removing their distortions due to positional fluctuations. This allows us to experimentally access the spatiotemporal fluctuations of local symmetries at a single-particle level in crystallization of a charged colloidal system near the body-centered cubic–face-centered cubic border. Thus, we successfully reveal the crucial roles of competing ordering in the initial selection of polymorphs and the final grain boundary motion toward the most stable state from a microscopic perspective.

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Revealing key structural features hidden in liquids and glasses

Cited by 204 publications

References 189 publications

Structural order as a genuine control parameter of dynamics in simple glass formers

Structural order as a genuine control parameter of dynamics in simple glass formers

Autonomously revealing hidden local structures in supercooled liquids

Revealing roles of competing local structural orderings in crystallization of polymorphic systems

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