Assessing the structural heterogeneity of supercooled liquids through community inference.

  title={Assessing the structural heterogeneity of supercooled liquids through community inference.},
  author={Joris Paret and Robert L. Jack and Daniele Coslovich},
  journal={The Journal of chemical physics},
  volume={152 14},
We present an information-theoretic approach inspired by distributional clustering to assess the structural heterogeneity of particulate systems. Our method identifies communities of particles that share a similar local structure by harvesting the information hidden in the spatial variation of two- or three-body static correlations. This corresponds to an unsupervised machine learning approach that infers communities solely from the particle positions and their species. We apply this method to… 

Averaging Local Structure to Predict the Dynamic Propensity in Supercooled Liquids.

This Letter proposes that the key structural ingredient to the GNN method is its ability to consider not only the local structure around a central particle, but also averaged structural features centered around nearby particles, and designs a significantly more efficient model that provides essentially the same predictive power at a fraction of the computational complexity.

Autonomously revealing hidden local structures in supercooled liquids

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, are demonstrated.

Dimensionality reduction of local structure in glassy binary mixtures.

Principal component analysis (PCA) is applied to several descriptors and evidence of a varying degree of structural heterogeneity across glassy mixtures is shown, indicating that glassy binary mixtures have a broad spectrum of structural features.

Exploring glassy dynamics with Markov state models from graph dynamical neural networks.

Using machine learning techniques, we introduce a Markov state model (MSM) for a model glass former that reveals structural heterogeneities and their slow dynamics by coarse-graining the molecular

Dynamics of supercooled liquids from static averaged quantities using machine learning

It is concluded that the memory function of supercooled liquids can be e-ectively parameterized as the sum of two stretched exponentials, which physically corresponds to two dominant relaxation modes.

Predicting dynamic heterogeneity in glass-forming liquids by physics-informed machine learning

We introduce GlassMLP, a machine learning framework using physics-informed structural input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep neural network to

Correlation of plastic events with local structure in jammed packings across spatial dimensions

Significance Mean-field theories, exact in the limit of infinite spatial dimensions, succeed in describing many features of glasses and amorphous solids in low dimensions, leading to considerable

Neural Networks Reveal the Impact of the Vibrational Dynamics in the Prediction of the Long-Time Mobility of Molecular Glassformers

Two neural networks (NN) are designed to predict the particle mobility of a molecular glassformer in a wide time window ranging from vibrational dynamics to structural relaxation. Both NNs are

Revisiting the single-saddle model for the β-relaxation of supercooled liquids.

The dynamics of glass-forming liquids display several outstanding features, such as two-step relaxation and dynamic heterogeneities, which are difficult to predict quantitatively from first

What do deep neural networks find in disordered structures of glasses?

Glass transitions are widely observed in various types of soft matter systems. However, the physical mechanism of these transitions remains elusive despite years of ambitious research. In particular,



Mutual information reveals multiple structural relaxation mechanisms in a model glass former

An information theoretic approach is introduced to determine correlations in displacement for particle relaxation encoded in the initial configuration of a glass-forming liquid and reveals a dynamic lengthscale similar to that associated with structural properties, which may resolve the discrepancy between structural and dynamic lengthscales.

Correlation of local order with particle mobility in supercooled liquids is highly system dependent.

The role of order-agnostic point-to-set correlations is investigated and it is revealed that they provide similar information content to local structure measures, at least in the system where local order is most pronounced.

Machine learning for autonomous crystal structure identification.

This work uses nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment, which yields unbiased structural information which allows them to quantify the crystalline character of particles near defects, grain boundaries, and interfaces.

Unsupervised learning for local structure detection in colloidal systems.

A simple, fast, and easy to implement unsupervised learning algorithm for detecting different local environments on a single-particle level in colloidal systems and using a neural-network-based autoencoder combined with Gaussian mixture models in order to autonomously group together similar environments.

Detecting hidden spatial and spatio-temporal structures in glasses and complex physical systems by multiresolution network clustering

A general method for characterizing the “natural” structures in complex physical systems via multi-scale network analysis based on “community detection” and identifies the dominant structures (disjoint or overlapping) and general length scales by analyzing extrema of the information theory measures.

Extraction of force-chain network architecture in granular materials using community detection.

By resolving individual force chains, this work quantifies statistical properties of force-chain shape and strength, which are potentially crucial diagnostics of bulk properties (including material stability).

Lifetimes and lengthscales of structural motifs in a model glassformer.

A strong, but indirect, correlation between static structural ordering and slow dynamics is found in a popular model glassformer, the Kob-Andersen binary Lennard-Jones mixture.

Information-theoretic measurements of coupling between structure and dynamics in glass formers.

The mutual information allows the influence of the liquid structure on the dynamics to be analyzed quantitatively as a function of time, showing that normal modes give the most useful predictions on short time scales while local energy and density are most strongly predictive at long times.

Identification of long-lived clusters and their link to slow dynamics in a model glass former.

It is found that most icosahedral clusters with a particular composition and arrangement of large and small particles are structural elements of the crystal and thus local crystalline ordering makes only a limited contribution to this process.

Understanding fragility in supercooled Lennard-Jones mixtures. I. Locally preferred structures.

The existence of systematic variations of isobaric fragility in different supercooled Lennard-Jones binary mixtures is revealed by molecular dynamics simulations. The connection between fragility and