The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A structural approach to relaxation in glassy liquids
Open accessAbstract
The relation between structure and dynamics in glasses is not fully understood. A new approach based on machine learning now reveals a correlation between softness—a structural property—and glassy dynamics. In contrast with crystallization, there is no noticeable structural change at the glass transition. Characteristic features of glassy dynamics that appear below an onset temperature, T0 (refs 1,2,3), are qualitatively captured by mean field theory4,5,6, which assumes uniform local structure. Studies of more realistic systems have found only weak correlations between structure and dynamics7,8,9,10,11. This raises the question: is structure important to glassy dynamics in three dimensions? We answer this question affirmatively, using machine learning to identify a new field, ‘softness’ which characterizes local structure and is strongly correlated with dynamics. We find that the onset of glassy dynamics at T0 corresponds to the onset of correlations between softness (that is, structure) and dynamics. Moreover, we construct a simple model of relaxation that agrees well with our simulation results, showing that a theory of the evolution of softness in time would constitute a theory of glassy dynamics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
Formation of Glasses from Liquids and Biopolymers
Angell, C. A. (1995) · Science · doi:10.1126/science.267.5206.1924 · Cited by 4,799
Supercooled liquids and the glass transition
Debenedetti, P. G., & Stillinger, F. H. (2001) · Nature · doi:10.1038/35065704 · Cited by 4,643
Analysis of Classical Statistical Mechanics by Means of Collective Coordinates
Percus, J. K., & Yevick, G. J. (1958) · Physical Review · doi:10.1103/physrev.110.1 · Cited by 2,856
Theoretical perspective on the glass transition and amorphous materials
Berthier, L., & Biroli, G. (2011) · Reviews of Modern Physics · doi:10.1103/revmodphys.83.587 · Cited by 2,133
Signatures of distinct dynamical regimes in the energy landscape of a glass-forming liquid
Sastry, S., Debenedetti, P. G., & Stillinger, F. H. (1998) · Nature · doi:10.1038/31189 · Cited by 814
Cited by
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215
Machine learning phases of matter
Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics · doi:10.1038/nphys4035 · Cited by 1,448
Unveiling the predictive power of static structure in glassy systems
Bapst, V., Keck, T., Grabska-Barwińska, A., Donner, C., Cubuk, E. D., Schoenholz, S. S., Obika, A., Nelson, A. W. R., Back, T., Hassabis, D., et al. (2020) · Nature Physics · doi:10.1038/s41567-020-0842-8 · Cited by 341
Self-citationPerim, E., Lee, D., Liu, Y., Toher, C., Gong, P., Li, Y., Simmons, W. N., Levy, O., Vlassak, J. J., Schroers, J., et al. (2016) · Nature Communications · doi:10.1038/ncomms12315 · Cited by 139
Machine learning meets quantum foundations: A brief survey
Bharti, K., Haug, T., Vedral, V., & Kwek, L.-C. (2020) · AVS Quantum Science · doi:10.1116/5.0007529 · Cited by 59
Metadata
- Authors
- Schoenholz, S. S., Cubuk, E. D., Sussman, D. M., Kaxiras, E., & Liu, A. J.
- Venue
- Nature Physics
- Published
- 2016-02-01
- Volume
- 12
- Issue
- 5
- Pages
- 469-471
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- OpenAlex
- W1653517664
- Cited by
- 461
- References
- 16
- FWCI
- 16.15
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A structural approach to relaxation in glassy liquids. https://app.cochat.ai/papers/10.1038/nphys3644
- The paper
- Schoenholz, S. S., Cubuk, E. D., Sussman, D. M., Kaxiras, E., & Liu, A. J. (2016). A structural approach to relaxation in glassy liquids. Nature Physics. https://doi.org/10.1038/nphys3644
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
