ArticleNature communications2020
Autonomously revealing hidden local structures in supercooled liquids.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Machine learning evaluation of structural descriptors for supercooled water.Communications chemistry · 2026Article
- On the geometry of topological defects in glasses.Nature communications · 2025Article
- Atomic-resolution imaging reveals nucleus-free crystallization in two-dimensional amorphous ice on graphite.Nature communications · 2025Article
- Calculating a Phase Diagram of a Simple Water Model Using Unsupervised Machine Learning on Simulation Data.Journal of chemical theory and computation · 2025Article
- Unsupervised learning of structural relaxation in supercooled liquids from short-term fluctuations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Structural Origin of Dynamic Heterogeneity in Supercooled Liquids.The journal of physical chemistry. B · 2025Review
- Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation.Journal of molecular modeling · 2024Article
- Structural origin of relaxation in dense colloidal suspensions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Machine learning unravels inherent structural patterns in Escherichia coli Hi-C matrices and predicts chromosome dynamics.Nucleic acids research · 2024Article
- Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.Biophysical journal · 2024Article
- Machine Learning Classification of Local Environments in Molecular Crystals.Journal of chemical theory and computation · 2024Article
- Revealing the hidden structure of disordered materials by parameterizing their local structural manifold.Nature communications · 2024Article
- Review
- Data-Driven Identification and Analysis of the Glass Transition in Polymer Melts.ACS macro letters · 2023Article
- Emergent structural correlations in dense liquids.PNAS nexus · 2023Article
- Dynamics and Time Scales of Higher-Order Correlations in Supercooled Colloidal Systems.The journal of physical chemistry letters · 2023Article
- Johari-GoldsteinProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Neural Networks Reveal the Impact of the Vibrational Dynamics in the Prediction of the Long-Time Mobility of Molecular Glassformers.International journal of molecular sciences · 2022Article
- Robust prediction of force chains in jammed solids using graph neural networks.Nature communications · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.