The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
Open accessAbstract
We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.
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Metadata
- Authors
- Cubuk, E. D., Schoenholz, S. S., Rieser, J. M., Malone, B. D., Rottler, J., Durian, D. J., Kaxiras, E., & Liu, A. J.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2015-03-09
- Volume
- 114
- Issue
- 10
- Pages
- 108001-108001
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2072637332
- Cited by
- 447
- References
- 18
- FWCI
- 10.39
- Type
- journal article
- Language
- en
- Open access
- green
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- Cubuk, E. D., Schoenholz, S. S., Rieser, J. M., Malone, B. D., Rottler, J., Durian, D. J., Kaxiras, E., & Liu, A. J. (2015). Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods. Physical Review Letters. https://doi.org/10.1103/physrevlett.114.108001
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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