Autonomously revealing hidden local structures in supercooled liquids
The origin of dynamical slowdown in disordered materials remains elusive, especially in the absence of obvious structural changes. Boattini et al. use unsupervised machine learning to reveal correlations between structural and dynamical heterogeneity in supercooled liquids.
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/fe002f9f60f94c47b33d4f89d83e76fb |
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| Sumario: | The origin of dynamical slowdown in disordered materials remains elusive, especially in the absence of obvious structural changes. Boattini et al. use unsupervised machine learning to reveal correlations between structural and dynamical heterogeneity in supercooled liquids. |
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