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Autonomously revealing hidden local structures in supercooled liquids
Emanuele Boattini1, Susana Marín-Aguilar2, Saheli Mitra2
1Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, Utrecht, Netherlands.
Nature Communications
|October 31, 2020
Summary
Machine learning identifies structural patterns in supercooled liquids without dynamics data. These structures correlate with slow particle movement, offering new insights into glassy material dynamics.
Area of Science:
- Condensed matter physics
- Materials science
- Statistical mechanics
Background:
- Understanding the relationship between structure and dynamics in supercooled liquids is a significant challenge.
- Previous research focused on identifying local structures and order parameters correlating with dynamics.
Purpose of the Study:
- To apply unsupervised machine learning to identify structural heterogeneities in glass-forming liquids.
- To develop purely structural order parameters without using dynamical information.
- To investigate the correlation between identified structural features and dynamical heterogeneities.
Main Methods:
- Utilized an unsupervised machine learning algorithm.
- Analyzed three archetypal glass-forming systems.
- Designed structural order parameters from single snapshots of the systems.
Main Results:
- The machine learning approach autonomously identified structural order parameters.
- These purely structural parameters showed strong correlations with dynamical heterogeneities.
- Structural characteristics associated with slow particles diminished with increasing distance from the glass transition.
Conclusions:
- Machine learning effectively detects structural patterns in disordered systems.
- This approach provides a novel method for understanding the structural basis of slow dynamics in glassy materials.
- The findings offer new avenues for research into the nature of supercooled liquids and glasses.
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