Neural-network-based order parameters for classification of binary hard-sphere crystal structures

Boattini, Emanuele; Ram, M SatyaSai; Smallenburg, Frank; Filion, Laura

doi:10.1080/00268976.2018.1483537

Cited by 35 publications

(38 citation statements)

References 30 publications

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“…Machine learning (ML) techniques are rapidly becoming a game-changer in the study of materials. Examples include speeding up computationally expensive calculations 5 , accurately distinguishing different crystal phases 6 , 7 , and even developing design rules for structural and material properties 8 . An exciting development is the design of UML algorithms that can autonomously classify particles based on patterns in their local environment 9 – 11 , even in disordered systems 12 .…”

Section: Introductionmentioning

confidence: 99%

Autonomously revealing hidden local structures in supercooled liquids

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

Autonomously revealing hidden local structures in supercooled liquids

et al. 2020

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“…In ref ( 60 ), a single-layer ANN, i . e ., only an input and output layer and no hidden layers, was employed to successfully classify the AB 13 phase from a binary fluid phase with a composition x L = 1/3 using the averaged bond order parameters as input.…”

Section: Resultsmentioning

confidence: 99%

An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB₁₃ Crystal

Coli

Dijkstra

2021

ACS Nano

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Colloidal suspensions of two species have the ability to form binary crystals under certain conditions. The hunt for these functional materials and the countless investigations on their formation process are justified by the plethora of synergetic and collective properties these binary superlattices show. Among the many crystal structures observed over the past decades, the highly exotic colloidal icosahedral AB 13 crystal was predicted to be stable in binary hard-sphere mixtures nearly 30 years ago, yet the kinetic pathway of how homogeneous nucleation occurs in this system is still unknown. Here we investigate binary nucleation of the AB 13 crystal from a binary fluid phase of nearly hard spheres. We calculate the nucleation barrier and nucleation rate as a function of supersaturation and draw a comparison with nucleation of single-component and other binary crystals. To follow the nucleation process, we employ a neural network to identify the AB 13 phase from the binary fluid phase and the competing fcc crystal with single-particle resolution and significant accuracy in the case of bulk phases. We show that AB 13 crystal nucleation proceeds via a coassembly process where large spheres and icosahedral small-sphere clusters simultaneously attach to the nucleus. Our results lend strong support for a classical pathway that is well-described by classical nucleation theory, even though the binary fluid phase is highly structured and exhibits local regions of high bond orientational order.

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“…14 Moreover, developments in the last few years have started to combine these local descriptions with supervised machine learning techniques in order to recognize specific crystal structures. [15][16][17][18] A very recent development has shown that this strategy can even be applied, with the aid of deep learning techniques, to simple coordinate information (instead of the standard local order descriptors). 19 In general, supervised learning strategies work very well to distinguish structures that we expect to form.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning for local structure detection in colloidal systems

Boattini

Dijkstra

Filion

2019

The Journal of Chemical Physics

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We introduce a simple, fast, and easy to implement unsupervised learning algorithm for detecting different local environments on a single-particle level in colloidal systems. In this algorithm, we use a vector of standard bond-orientational order parameters to describe the local environment of each particle. We then use a neural-network-based autoencoder combined with Gaussian mixture models in order to autonomously group together similar environments. We test the performance of the method on snapshots of a wide variety of colloidal systems obtained via computer simulations, ranging from simple isotropically interacting systems to binary mixtures, and even anisotropic hard cubes. Additionally, we look at a variety of common self-assembled situations such as fluid-crystal and crystal-crystal coexistences, grain boundaries, and nucleation. In all cases, we are able to identify the relevant local environments to a similar precision as "standard," manually tuned, and system-specific, order parameters. In addition to classifying such environments, we also use the trained autoencoder in order to determine the most relevant bond orientational order parameters in the systems analyzed.

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Neural-network-based order parameters for classification of binary hard-sphere crystal structures

Cited by 35 publications

References 30 publications

Autonomously revealing hidden local structures in supercooled liquids

Autonomously revealing hidden local structures in supercooled liquids

An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB₁₃ Crystal

Unsupervised learning for local structure detection in colloidal systems

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Neural-network-based order parameters for classification of binary hard-sphere crystal structures

Cited by 35 publications

References 30 publications

Autonomously revealing hidden local structures in supercooled liquids

Autonomously revealing hidden local structures in supercooled liquids

An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB13 Crystal

Unsupervised learning for local structure detection in colloidal systems

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Product

Resources

About

An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB₁₃ Crystal