1
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Sahu R, Sharma M, Schall P, Maitra Bhattacharyya S, Chikkadi V. Structural origin of relaxation in dense colloidal suspensions. Proc Natl Acad Sci U S A 2024; 121:e2405515121. [PMID: 39382997 DOI: 10.1073/pnas.2405515121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 08/22/2024] [Indexed: 10/11/2024] Open
Abstract
Amorphous solids relax via slow molecular rearrangement induced by thermal fluctuations or applied stress. Microscopic structural signatures predicting these structural relaxations have been long searched for but have so far only been found in dynamic quantities such as vibrational quasi-localized soft modes or with structurally trained neural networks. A physically meaningful structural quantity remains elusive. Here, we introduce a structural order parameter derived from the mean-field caging potential experienced by the particles due to their neighbors, which reliably predicts the occurrence of structural relaxations. The structural parameter, derived from density functional theory, provides a measure of susceptibility to particle rearrangements that can effectively identify weak or defect-like regions in disordered systems. Using experiments on dense colloidal suspensions, we demonstrate a strong correlation between this order parameter and the structural relaxations of the amorphous solid. In quiescent suspensions, this correlation increases with density, when particle rearrangements become rarer and more localized. In sheared suspensions, the order parameter reliably pinpoints shear transformations; the applied shear weakens the caging potential due to shear-induced structural distortions, causing the proliferation of plastic deformation at structurally weak regions. Our work paves the way to a structural understanding of the relaxation of a wide range of amorphous solids, from suspensions to metallic glasses.
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Affiliation(s)
- Ratimanasee Sahu
- Physics Division, Indian Institute of Science Education and Research Pune, Pune 411008, India
| | - Mohit Sharma
- Polymer Science and Engineering Division, CSIR - National Chemical Laboratory, Pune 411008, India
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Peter Schall
- Institute of Physics, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
| | - Sarika Maitra Bhattacharyya
- Polymer Science and Engineering Division, CSIR - National Chemical Laboratory, Pune 411008, India
- Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Vijayakumar Chikkadi
- Physics Division, Indian Institute of Science Education and Research Pune, Pune 411008, India
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2
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de Jager M, Kolbeck PJ, Vanderlinden W, Lipfert J, Filion L. Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning. Biophys J 2024; 123:3231-3241. [PMID: 39044429 PMCID: PMC11427786 DOI: 10.1016/j.bpj.2024.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/18/2024] [Accepted: 07/18/2024] [Indexed: 07/25/2024] Open
Abstract
Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here, we present a simple and generic coarse-grained model for DNA-protein and protein-protein interactions and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand canonical ensemble, and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal-component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and protein-induced DNA compaction and provides a systematic and semi-quantitative approach for analyzing their mesoscale complexes.
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Affiliation(s)
- Marjolein de Jager
- Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands.
| | - Pauline J Kolbeck
- Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands; Department of Physics and Center for NanoScience, LMU, Munich, Germany
| | - Willem Vanderlinden
- Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands; Department of Physics and Center for NanoScience, LMU, Munich, Germany; School of Physics and Astronomy, University of Edinburgh, Scotland, United Kingdom
| | - Jan Lipfert
- Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands; Department of Physics and Center for NanoScience, LMU, Munich, Germany
| | - Laura Filion
- Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, the Netherlands
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3
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Bera P, Mondal J. Machine learning unravels inherent structural patterns in Escherichia coli Hi-C matrices and predicts chromosome dynamics. Nucleic Acids Res 2024:gkae749. [PMID: 39217471 DOI: 10.1093/nar/gkae749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
High dimensional nature of the chromosomal conformation contact map ('Hi-C Map'), even for microscopically small bacterial cell, poses challenges for extracting meaningful information related to its complex organization. Here we first demonstrate that an artificial deep neural network-based machine-learnt (ML) low-dimensional representation of a recently reported Hi-C interaction map of archetypal bacteria Escherichia coli can decode crucial underlying structural pattern. The ML-derived representation of Hi-C map can automatically detect a set of spatially distinct domains across E. coli genome, sharing reminiscences of six putative macro-domains previously posited via recombination assay. Subsequently, a ML-generated model assimilates the intricate relationship between large array of Hi-C-derived chromosomal contact probabilities and respective diffusive dynamics of each individual chromosomal gene and identifies an optimal number of functionally important chromosomal contact-pairs that are majorly responsible for heterogenous, coordinate-dependent sub-diffusive motions of chromosomal loci. Finally, the ML models, trained on wild-type E. coli show-cased its predictive capabilities on mutant bacterial strains, shedding light on the structural and dynamic nuances of ΔMatP30MM and ΔMukBEF22MM chromosomes. Overall our results illuminate the power of ML techniques in unraveling the complex relationship between structure and dynamics of bacterial chromosomal loci, promising meaningful connections between ML-derived insights and biological phenomena.
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Affiliation(s)
- Palash Bera
- Tata Institute of Fundamental Research Hyderabad, Telangana 500046, India
| | - Jagannath Mondal
- Tata Institute of Fundamental Research Hyderabad, Telangana 500046, India
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4
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Kuroshima D, Kilgour M, Tuckerman ME, Rogal J. Machine Learning Classification of Local Environments in Molecular Crystals. J Chem Theory Comput 2024; 20:6197-6206. [PMID: 38959410 PMCID: PMC11270820 DOI: 10.1021/acs.jctc.4c00418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 07/05/2024]
Abstract
Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the first case, we follow our earlier work on graph learning in molecular crystals, deploying an atomistic graph convolutional network combined with molecule-wise aggregation to enable per-molecule environmental classification. For the second model, we develop a new set of descriptors based on symmetry functions combined with a point-vector representation of the molecules, encoding information about the positions and relative orientations of the molecule. We demonstrate very high classification accuracy for both approaches on urea and nicotinamide crystal polymorphs and practical applications to the analysis of dynamical trajectory data for nanocrystals and solid-solid interfaces. Both architectures are applicable to a wide range of molecules and diverse topologies, providing an essential step in the exploration of complex condensed matter phenomena.
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Affiliation(s)
- Daisuke Kuroshima
- Department
of Chemistry, New York University (NYU), New York, New York 10003, United States
| | - Michael Kilgour
- Department
of Chemistry, New York University (NYU), New York, New York 10003, United States
| | - Mark E. Tuckerman
- Department
of Chemistry, New York University (NYU), New York, New York 10003, United States
- Courant
Institute of Mathematical Sciences, New
York University, New York, New York 10012, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Rd. North, Shanghai 200062, China
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
| | - Jutta Rogal
- Department
of Chemistry, New York University (NYU), New York, New York 10003, United States
- Fachbereich
Physik, Freie Universität Berlin, Berlin 14195, Germany
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5
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Haga T. Machine learning analysis of dimensional reduction conjecture for nonequilibrium Berezinskii-Kosterlitz-Thouless transition in three dimensions. Phys Rev E 2024; 110:014137. [PMID: 39160920 DOI: 10.1103/physreve.110.014137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 07/01/2024] [Indexed: 08/21/2024]
Abstract
We investigate the recently proposed dimensional reduction conjecture in driven disordered systems using a machine learning technique. The conjecture states that a static snapshot of a disordered system driven at a constant velocity is equal to a space-time trajectory of its lower-dimensional pure counterpart. This suggests that the three-dimensional (3D) random field XY model exhibits the Berezinskii-Kosterlitz-Thouless transition when driven out of equilibrium. To verify the conjecture directly by observing configurations of the system, we utilize the capacity of neural networks to detect subtle features of images. Specifically, we train neural networks to differentiate snapshots of the 3D driven random field XY model from space-time trajectories of the two-dimensional pure XY model. Our results demonstrate that the network cannot distinguish between the two, confirming the dimensional reduction conjecture.
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6
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Zhang X, Mochizuki K. Hydrogen-bond linking is crucial for growing ice VII embryos. J Chem Phys 2024; 160:214506. [PMID: 38832740 DOI: 10.1063/5.0205566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
We use molecular dynamics simulations to examine the homogeneous nucleation of ice VII from metastable liquid water. An unsupervised machine learning classification identifies two distinct local structures composing Ice VII nuclei. The seeding method, combined with the classical nucleation theory (CNT), predicts the solid-liquid interfacial free energy, consistent with the value from the mold integration method. Meanwhile, the nucleation rates estimated from the CNT framework and brute force spontaneous nucleations are inconsistent, and we discuss the reasons for this discrepancy. Structural and dynamical heterogeneities suggest that the potential birthplace for an ice VII embryo is relatively ordered, although not necessarily relatively immobile. Moreover, we demonstrate that without the formation of hydrogen-bond links, ice VII embryos do not grow.
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Affiliation(s)
- Xuan Zhang
- Department of Chemistry, Zhejiang University, Hangzhou 310028, People's Republic of China
| | - Kenji Mochizuki
- Department of Chemistry, Zhejiang University, Hangzhou 310028, People's Republic of China
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7
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Hardin TJ, Chandross M, Meena R, Fajardo S, Giovanis D, Kevrekidis I, Falk ML, Shields MD. Revealing the hidden structure of disordered materials by parameterizing their local structural manifold. Nat Commun 2024; 15:4424. [PMID: 38789423 PMCID: PMC11126625 DOI: 10.1038/s41467-024-48449-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.
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Affiliation(s)
- Thomas J Hardin
- Material, Physical, and Chemical Sciences Center, Sandia National Laboratories, Albuquerque, NM, USA.
| | - Michael Chandross
- Material, Physical, and Chemical Sciences Center, Sandia National Laboratories, Albuquerque, NM, USA
| | - Rahul Meena
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Spencer Fajardo
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Dimitris Giovanis
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ioannis Kevrekidis
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Michael L Falk
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Shields
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
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8
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Dyre JC. Solid-that-Flows Picture of Glass-Forming Liquids. J Phys Chem Lett 2024; 15:1603-1617. [PMID: 38306474 PMCID: PMC10875679 DOI: 10.1021/acs.jpclett.3c03308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 02/04/2024]
Abstract
This perspective article reviews arguments that glass-forming liquids are different from those of standard liquid-state theory, which typically have a viscosity in the mPa·s range and relaxation times on the order of picoseconds. These numbers grow dramatically and become 1012 - 1015 times larger for liquids cooled toward the glass transition. This translates into a qualitative difference, and below the "solidity length" which is roughly one micron at the glass transition, a glass-forming liquid behaves much like a solid. Recent numerical evidence for the solidity of ultraviscous liquids is reviewed, and experimental consequences are discussed in relation to dynamic heterogeneity, frequency-dependent linear-response functions, and the temperature dependence of the average relaxation time.
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Affiliation(s)
- Jeppe C Dyre
- "Glass and Time", IMFUFA, Dept. of Sciences, Roskilde University, P.O. Box 260, DK-4000 Roskilde, Denmark
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9
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Krishnan G, Harbola U. Structure of quantum supercooled liquids. Phys Rev E 2024; 109:014115. [PMID: 38366528 DOI: 10.1103/physreve.109.014115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/20/2023] [Indexed: 02/18/2024]
Abstract
Supercooled liquids show a drastic slowdown in the dynamics with decreasing temperature, while their structure remains similar to that of normal liquids. In this paper, the structural features in a quantum supercooled liquid are explored in terms of cages defined using the Voronoi polyhedra and characterized in terms of their volumes and geometries. The cage volume fluctuations are sensitive to the quantum effects, and decrease as the glass transition is approached by varying the quantumness. This is in contrast to the classical case where the volumes are insensitive to temperature variations as one approaches the transition. The cage geometry becomes more spherical upon increasing quantumness from zero, pushing the system closer to the glass transition. The cage geometry is found to be significantly correlated with asymmetry in the position uncertainty of the caged particle in the strongly quantum regime.
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Affiliation(s)
- Gopika Krishnan
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India
| | - Upendra Harbola
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India
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10
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Zhang L, Chen J, Zhang H, Huang D. The prediction of dynamical quantities in granular avalanches based on graph neural networks. J Chem Phys 2023; 159:214901. [PMID: 38038211 DOI: 10.1063/5.0172022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
The study of granular avalanches in rotating drums is not only essential to understanding various complex behaviors of interest in granular media from a scientific perspective; it also has valuable applications in regard to industrial processes and geological catastrophes. Despite decades of research studies on avalanches, a proper understanding of their dynamic properties still remains a great challenge to scientists due to a lack of state-of-the-art techniques. In this study, we accurately predict the avalanche dynamic features of three-dimensional granular materials in rotating drums, by using graph neural networks on the basis of their initial static microstructures alone. We find that our method is robust to changes in various model parameters, such as the interaction potential, size polydispersity, and noise in particle coordinates. In addition, with the grain-scale velocities obtained either from our network or from numerical simulations, we find an approximately equal and strong correlation between the global velocity and global velocity fluctuation in our 3D granular avalanche systems, which further demonstrates the predictive power of our trained graph neural networks to uncover the fundamental physics of granular avalanches. We expect our method to provide more insight into the avalanche dynamics of granular materials and other amorphous systems in the future.
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Affiliation(s)
- Ling Zhang
- School of Automation, Central South University, Changsha 410083, China
| | - Jianfeng Chen
- School of Automation, Central South University, Changsha 410083, China
| | - Hang Zhang
- School of Automation, Central South University, Changsha 410083, China
| | - Duan Huang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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11
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Jiang X, Tian Z, Li K, Hu W. A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure. J Chem Phys 2023; 159:144504. [PMID: 37830454 DOI: 10.1063/5.0162463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/19/2023] [Indexed: 10/14/2023] Open
Abstract
Modeling the dynamics of glassy systems has been challenging in physics for several decades. Recent studies have shown the efficacy of Graph Neural Networks (GNNs) in capturing particle dynamics from the graph structure of glassy systems. However, current GNN methods do not take the dynamic patterns established by neighboring particles explicitly into account. In contrast to these approaches, this paper introduces a novel dynamical parameter termed "smoothness" based on the theory of graph signal processing, which explores the dynamic patterns from a graph perspective. Present graph-based approaches encode structural features without considering smoothness constraints, leading to a weakened correlation between structure and dynamics, particularly on short timescales. To address this limitation, we propose a Geometry-enhanced Graph Neural Network (Geo-GNN) to learn the smoothness of dynamics. Results demonstrate that our method outperforms state-of-the-art baselines in predicting glassy dynamics. Ablation studies validate the effectiveness of each proposed component in capturing smoothness within dynamics. These findings contribute to a deeper understanding of the interplay between glassy dynamics and static structure.
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Affiliation(s)
- Xiao Jiang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Zean Tian
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Kenli Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Wangyu Hu
- College of Materials Science and Engineering, Hunan University, Changsha, China
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12
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de Jager M, Smallenburg F, Filion L. In search of a precursor for crystal nucleation of hard and charged colloids. J Chem Phys 2023; 159:134902. [PMID: 37787142 DOI: 10.1063/5.0161356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023] Open
Abstract
The interplay between crystal nucleation and the structure of the metastable fluid has been a topic of significant debate over recent years. In particular, it has been suggested that even in simple model systems such as hard or charged colloids, crystal nucleation might be foreshadowed by significant fluctuations in local structure around the location where the nucleus first arises. We investigate this using computer simulations of spontaneous nucleation events in both hard and charged colloidal systems. To detect local structural variations, we use both standard and unsupervised machine learning methods capable of finding hidden structures in the metastable fluid phase. We track numerous nucleation events for the face-centered cubic and body-centered cubic crystals on a local level and demonstrate that all signs of crystallinity emerge simultaneously from the very start of the nucleation process. We thus conclude that we observe no precursor for the crystal nucleation of hard and charged colloids.
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Affiliation(s)
- Marjolein de Jager
- Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
| | - Frank Smallenburg
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405 Orsay, France
| | - Laura Filion
- Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
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13
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Richard D, Kapteijns G, Lerner E. Detecting low-energy quasilocalized excitations in computer glasses. Phys Rev E 2023; 108:044124. [PMID: 37978582 DOI: 10.1103/physreve.108.044124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 09/15/2023] [Indexed: 11/19/2023]
Abstract
Soft, quasilocalized excitations (QLEs) are known to generically emerge in a broad class of disordered solids and to govern many facets of the physics of glasses, from wave attenuation to plastic instabilities. In view of this key role of QLEs, shedding light upon several open questions in glass physics depends on the availability of computational tools that allow one to study QLEs' statistical mechanics. The latter is a formidable task since harmonic analyses are typically contaminated by hybridizations of QLEs with phononic excitations at low frequencies, obscuring a clear picture of QLEs' abundance, typical frequencies, and other important micromechanical properties. Here we present an efficient algorithm to detect the field of quasilocalized excitations in structural computer glasses. The algorithm introduced takes a computer-glass sample as input and outputs a library of QLEs embedded in that sample. We demonstrate the power of the algorithm by reporting the spectrum of glassy excitations in two-dimensional computer glasses featuring a huge range of mechanical stability, which is inaccessible using conventional harmonic analyses due to phonon hybridizations. Future applications are discussed.
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Affiliation(s)
- David Richard
- Univ. Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France
| | - Geert Kapteijns
- Institute for Theoretical Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Edan Lerner
- Institute for Theoretical Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
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14
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Kim EC, Chun DJ, Park CB, Sung BJ. Machine learning predicts the glass transition of two-dimensional colloids besides medium-range crystalline order. Phys Rev E 2023; 108:044602. [PMID: 37978710 DOI: 10.1103/physreve.108.044602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023]
Abstract
We employ only the positions of colloidal particles and construct machine learning (ML) models to test the presence of structural order in glass transition for two kinds of two-dimensional (2D) colloids: 2D polydisperse colloids (PC) with medium-range crystalline order (MRCO) and 2D binary colloids (BC) without MRCO. ML models predict the glass transition of 2D colloids successfully without any information on MRCO. Even certain ML models trained with BC predict the glass transition of PC successfully, thus suggesting that universal structural characteristics would exist besides MRCO.
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Affiliation(s)
- Eun Cheol Kim
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Dong Jae Chun
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Chung Bin Park
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Bong June Sung
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
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15
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Banerjee A, Iscen A, Kremer K, Kukharenko O. Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning. J Chem Phys 2023; 159:074108. [PMID: 37602800 DOI: 10.1063/5.0151156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 07/27/2023] [Indexed: 08/22/2023] Open
Abstract
The functionality of many polymeric materials depends on their glass transition temperatures (Tg). In computer simulations, Tg is often calculated from the gradual change in macroscopic properties. Precise determination of this change depends on the fitting protocols. We previously proposed a robust data-driven approach to determine Tg from the molecular dynamics simulation data of a coarse-grained semiflexible polymer model. In contrast to the global macroscopic properties, our method relies on high-resolution microscopic details. Here, we demonstrate the generality of our approach by using various dimensionality reduction and clustering methods and apply it to an atomistic model of acrylic polymers. Our study reveals the explicit contribution of the side chain and backbone residues in influencing the determination of the glass transition temperature.
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Affiliation(s)
- Atreyee Banerjee
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Aysenur Iscen
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Kurt Kremer
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
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16
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Kerr Winter M, Pihlajamaa I, Debets VE, Janssen LMC. A deep learning approach to the measurement of long-lived memory kernels from generalized Langevin dynamics. J Chem Phys 2023; 158:244115. [PMID: 37366311 DOI: 10.1063/5.0149764] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023] Open
Abstract
Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalized Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via, e.g., a numerical inverse Laplace transform remains a herculean task. Here, we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As a proof-of-principle, we focus on the notoriously long-lived memory effects of glass-forming systems, which have proved a major challenge to existing methods. In particular, we learn the operator mapping dynamics to memory kernels from a training set generated with the Mode-Coupling Theory (MCT) of hard spheres. Our DNNs are remarkably robust against noise, in contrast to conventional techniques. Furthermore, we demonstrate that a network trained on data generated from analytic theory (hard-sphere MCT) generalizes well to data from simulations of a different system (Brownian Weeks-Chandler-Andersen particles). Finally, we train a network on a set of phenomenological kernels and demonstrate its effectiveness in generalizing to both unseen phenomenological examples and supercooled hard-sphere MCT data. We provide a general pipeline, KernelLearner, for training networks to extract memory kernels from any non-Markovian system described by a GLE. The success of our DNN method applied to noisy glassy systems suggests that deep learning can play an important role in the study of dynamical systems with memory.
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Affiliation(s)
- Max Kerr Winter
- Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Ilian Pihlajamaa
- Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Vincent E Debets
- Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Liesbeth M C Janssen
- Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
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17
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Jung G, Biroli G, Berthier L. Predicting Dynamic Heterogeneity in Glass-Forming Liquids by Physics-Inspired Machine Learning. PHYSICAL REVIEW LETTERS 2023; 130:238202. [PMID: 37354408 DOI: 10.1103/physrevlett.130.238202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/07/2023] [Accepted: 05/17/2023] [Indexed: 06/26/2023]
Abstract
We introduce GlassMLP, a machine learning framework using physics-inspired structural input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep neural network to atomistic models in 2D and 3D. Its performance is better than the state of the art while being more parsimonious in terms of training data and fitting parameters. GlassMLP quantitatively predicts four-point dynamic correlations and the geometry of dynamic heterogeneity. Transferability across system sizes allows us to efficiently probe the temperature evolution of spatial dynamic correlations, revealing a profound change with temperature in the geometry of rearranging regions.
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Affiliation(s)
- Gerhard Jung
- Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, 34095 Montpellier, France
| | - Giulio Biroli
- Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Ludovic Berthier
- Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, 34095 Montpellier, France
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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18
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Pihlajamaa I, Laudicina CCL, Luo C, Janssen LMC. Emergent structural correlations in dense liquids. PNAS NEXUS 2023; 2:pgad184. [PMID: 37342651 PMCID: PMC10279420 DOI: 10.1093/pnasnexus/pgad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/23/2023] [Indexed: 06/23/2023]
Abstract
The complete quantitative description of the structure of dense and supercooled liquids remains a notoriously difficult problem in statistical physics. Most studies to date focus solely on two-body structural correlations, and only a handful of papers have sought to consider additional three-body correlations. Here, we go beyond the state of the art by extracting many-body static structure factors from molecular dynamics simulations and by deriving accurate approximations up to the six-body structure factor via density functional theory. We find that supercooling manifestly increases four-body correlations, akin to the two- and three-body case. However, at small wave numbers, we observe that the four-point structure of a liquid drastically changes upon supercooling, both qualitatively and quantitatively, which is not the case in two-point structural correlations. This indicates that theories of the structure or dynamics of dense liquids should incorporate many-body correlations beyond the two-particle level to fully capture their intricate behavior.
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Affiliation(s)
| | | | - Chengjie Luo
- Soft Matter & Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, The Netherlands
- Max Planck Institute for Dynamics and Self-Organization, Göttingen 37077, Germany
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19
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Striker NN, Lokteva I, Dartsch M, Dallari F, Goy C, Westermeier F, Markmann V, Hövelmann SC, Grübel G, Lehmkühler F. Dynamics and Time Scales of Higher-Order Correlations in Supercooled Colloidal Systems. J Phys Chem Lett 2023; 14:4719-4725. [PMID: 37171882 DOI: 10.1021/acs.jpclett.3c00631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
The dynamics and time scales of higher-order correlations are studied in supercooled colloidal systems. A combination of X-ray photon correlation spectroscopy (XPCS) and X-ray cross-correlation analysis (XCCA) shows the typical slowing of the dynamics of a hard sphere system when approaching the glass transition. The time scales of higher-order correlations are probed using a novel time correlation function gC, tracking the time evolution of cross-correlation function C. With an increasing volume fraction, the ratio of relaxation times of gC to the standard individual particle relaxation time obtained by XPCS increases from ∼0.4 to ∼0.9. While a value of ∼0.5 is expected for free diffusion, the increasing values suggest that the local orders within the sample are becoming more long-lived for larger volume fractions. Furthermore, the dynamics of local order is more heterogeneous than the individual particle dynamics. These results indicate that not only the presence but also the lifetime of locally favored structures increases close to the glass transition.
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Affiliation(s)
- Nele N Striker
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Irina Lokteva
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
- The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Michael Dartsch
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
- The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Francesco Dallari
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Claudia Goy
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Fabian Westermeier
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Verena Markmann
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Svenja C Hövelmann
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
- Institut für Experimentelle und Angewandte Physik, Christian-Albrechts-Universität zu Kiel, Leibnizstraße 19, 24098 Kiel, Germany
| | - Gerhard Grübel
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
- The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Felix Lehmkühler
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
- The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany
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20
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Banerjee A, Hsu HP, Kremer K, Kukharenko O. Data-Driven Identification and Analysis of the Glass Transition in Polymer Melts. ACS Macro Lett 2023:679-684. [PMID: 37167550 DOI: 10.1021/acsmacrolett.2c00749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Understanding the nature of glass transition, as well as the precise estimation of the glass transition temperature for polymeric materials, remains open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information on individual chains. It clearly identifies the glass transition temperature of polymer melts of weakly semiflexible chains. By combining principal component analysis and clustering, we identify the glass transition temperature in the asymptotic limit even from relatively short time trajectories, which just reach into the Rouse-like monomer displacement regime. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain's behavior: from conformational rearrangement above to small fluctuations below the glass transition temperature. Our approach is straightforward to apply and should be applicable to other polymeric glass-forming liquids.
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Affiliation(s)
- Atreyee Banerjee
- Theory Department, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Hsiao-Ping Hsu
- Theory Department, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Kurt Kremer
- Theory Department, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Oleksandra Kukharenko
- Theory Department, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
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21
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Alkemade RM, Smallenburg F, Filion L. Improving the prediction of glassy dynamics by pinpointing the local cage. J Chem Phys 2023; 158:134512. [PMID: 37031101 DOI: 10.1063/5.0144822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023] Open
Abstract
The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here, we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To achieve this, we introduce a method to pinpoint the cage state of the initial configuration-i.e., the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime.
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Affiliation(s)
- Rinske M Alkemade
- Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, Utrecht, Netherlands
| | - Frank Smallenburg
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405 Orsay, France
| | - Laura Filion
- Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, Utrecht, Netherlands
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22
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Fayen E, Impéror-Clerc M, Filion L, Foffi G, Smallenburg F. Self-assembly of dodecagonal and octagonal quasicrystals in hard spheres on a plane. SOFT MATTER 2023; 19:2654-2663. [PMID: 36971334 DOI: 10.1039/d3sm00179b] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Hard spheres are one of the most fundamental model systems in soft matter physics, and have been instrumental in shedding light on nearly every aspect of classical condensed matter. Here, we add one more important phase to the list that hard spheres form: quasicrystals. Specifically, we use simulations to show that an extremely simple, purely entropic model system, consisting of two sizes of hard spheres resting on a flat plane, can spontaneously self-assemble into two distinct random-tiling quasicrystal phases. The first quasicrystal is a dodecagonal square-triangle tiling, commonly observed in a large variety of colloidal systems. The second quasicrystal has, to our knowledge, never been observed in either experiments or simulations. It exhibits octagonal symmetry, and consists of three types of tiles: triangles, small squares, and large squares, whose relative concentration can be continuously varied by tuning the number of smaller spheres present in the system. The observed tile composition of the self-assembled quasicrystals agrees very well with the theoretical prediction we obtain by considering the four-dimensional (lifted) representation of the quasicrystal. Both quasicrystal phases form reliably and rapidly over a significant part of parameter space. Our results demonstrate that entropy combined with a set of geometrically compatible, densely packed tiles can be sufficient ingredients for the self-assembly of colloidal quasicrystals.
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Affiliation(s)
- Etienne Fayen
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405, Orsay, France.
| | - Marianne Impéror-Clerc
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405, Orsay, France.
| | - Laura Filion
- Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands
| | - Giuseppe Foffi
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405, Orsay, France.
| | - Frank Smallenburg
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405, Orsay, France.
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23
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Shiraishi K, Mizuno H, Ikeda A. Johari-Goldstein β relaxation in glassy dynamics originates from two-scale energy landscape. Proc Natl Acad Sci U S A 2023; 120:e2215153120. [PMID: 36989301 PMCID: PMC10083593 DOI: 10.1073/pnas.2215153120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 02/17/2023] [Indexed: 03/30/2023] Open
Abstract
Supercooled liquids undergo complicated structural relaxation processes, which have been a long-standing problem in both experimental and theoretical aspects of condensed matter physics. In particular, past experiments widely observed for many types of molecular liquids that relaxation dynamics separated into two distinct processes at low temperatures. One of the possible interpretations is that this separation originates from the two-scale hierarchical topography of the potential energy landscape; however, it has never been verified. Molecular dynamics simulations are a promising approach to tackle this issue, but we must overcome laborious difficulties. First, we must handle a model of molecular liquids that is computationally demanding compared to simple spherical models, which have been intensively studied but show only a slower process: α relaxation. Second, we must reach a sufficiently low-temperature regime where the two processes become well-separated. Here, we handle an asymmetric dimer system that exhibits a faster process: Johari-Goldstein β relaxation. Then, we employ the parallel tempering method to access the low-temperature regime. These laborious efforts enable us to investigate the potential energy landscape in detail and unveil the first direct evidence of the topographic hierarchy that induces the β relaxation. We also successfully characterize the microscopic motions of particles during each relaxation process. Finally, we study the correlation between low-frequency modes and two relaxation processes. Our results establish a fundamental and comprehensive understanding of experimentally observed relaxation dynamics in supercooled liquids.
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Affiliation(s)
- Kumpei Shiraishi
- Graduate School of Arts and Sciences, University of Tokyo, Komaba, Tokyo153-8902, Japan
| | - Hideyuki Mizuno
- Graduate School of Arts and Sciences, University of Tokyo, Komaba, Tokyo153-8902, Japan
| | - Atsushi Ikeda
- Graduate School of Arts and Sciences, University of Tokyo, Komaba, Tokyo153-8902, Japan
- Research Center for Complex Systems Biology, Universal Biology Institute, University of Tokyo, Komaba, Tokyo153-8902, Japan
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24
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Verde AR, Alarcón LM, Appignanesi GA. Correlations between defect propensity and dynamical heterogeneities in supercooled water. J Chem Phys 2023; 158:114502. [PMID: 36948825 DOI: 10.1063/5.0139118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
A salient feature of supercooled liquids consists in the dramatic dynamical slowdown they undergo as temperature decreases while no significant structural change is evident. These systems also present dynamical heterogeneities (DH): certain molecules, spatially arranged in clusters, relax various orders of magnitude faster than the others. However, again, no static quantity (such as structural or energetic measures) shows strong direct correlations with such fast-moving molecules. In turn, the dynamic propensity approach, an indirect measure that quantifies the tendency of the molecules to move in a given structural configuration, has revealed that dynamical constraints, indeed, originate from the initial structure. Nevertheless, this approach is not able to elicit which structural quantity is, in fact, responsible for such a behavior. In an effort to remove dynamics from its definition in favor of a static quantity, an energy-based propensity has also been developed for supercooled water, but it could only find positive correlations between the lowest-energy and the least-mobile molecules, while no correlations could be found for those more relevant mobile molecules involved in the DH clusters responsible for the system's structural relaxation. Thus, in this work, we shall define a defect propensity measure based on a recently introduced structural index that accurately characterizes water structural defects. We shall show that this defect propensity measure provides positive correlations with dynamic propensity, being also able to account for the fast-moving molecules responsible for the structural relaxation. Moreover, time dependent correlations will show that defect propensity represents an appropriate early-time predictor of the long-time dynamical heterogeneity.
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Affiliation(s)
- Alejandro R Verde
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Avenida Alem 1253, 8000 Bahía Blanca, Argentina
| | - Laureano M Alarcón
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Avenida Alem 1253, 8000 Bahía Blanca, Argentina
| | - Gustavo A Appignanesi
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Avenida Alem 1253, 8000 Bahía Blanca, Argentina
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25
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Xu WS, Sun ZY. A Thermodynamic Perspective on Polymer Glass Formation. CHINESE JOURNAL OF POLYMER SCIENCE 2023. [DOI: 10.1007/s10118-023-2951-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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26
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Shiba H, Hanai M, Suzumura T, Shimokawabe T. BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion. J Chem Phys 2023; 158:084503. [PMID: 36859106 DOI: 10.1063/5.0129791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
Recent developments in machine learning have enabled accurate predictions of the dynamics of slow structural relaxation in glass-forming systems. However, existing machine learning models for these tasks are mostly designed such that they learn a single dynamic quantity and relate it to the structural features of glassy liquids. In this study, we propose a graph neural network model, "BOnd TArgeting Network," that learns relative motion between neighboring pairs of particles, in addition to the self-motion of particles. By relating the structural features to these two different dynamical variables, the model autonomously acquires the ability to discern how the self motion of particles undergoing slow relaxation is affected by different dynamical processes, strain fluctuations and particle rearrangements, and thus can predict with high precision how slow structural relaxation develops in space and time.
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Affiliation(s)
- Hayato Shiba
- Information Technology Center, University of Tokyo, Chiba 277-0882, Japan
| | - Masatoshi Hanai
- Information Technology Center, University of Tokyo, Chiba 277-0882, Japan
| | - Toyotaro Suzumura
- Information Technology Center, University of Tokyo, Chiba 277-0882, Japan
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27
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Laudicina CCL, Luo C, Miyazaki K, Janssen LMC. Dynamical susceptibilities near ideal glass transitions. Phys Rev E 2022; 106:064136. [PMID: 36671198 DOI: 10.1103/physreve.106.064136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Building on the recently derived inhomogeneous mode-coupling theory, we extend the generalized mode-coupling theory of supercooled liquids to inhomogeneous environments. This provides a first-principles-based, systematic, and rigorous way of deriving high-point dynamical susceptibilities from variations of the many-body dynamic structure factors with respect to their conjugate field. This framework allows for a fully microscopic possibility to probe for collective relaxation mechanisms in supercooled liquids near the mode-coupling glass transition. The behavior of these dynamical susceptibilities is then studied in the context of simplified self-consistent relaxation models.
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Affiliation(s)
- Corentin C L Laudicina
- Soft Matter & Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, The Netherlands
| | - Chengjie Luo
- Soft Matter & Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, The Netherlands
| | | | - Liesbeth M C Janssen
- Soft Matter & Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, The Netherlands
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28
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Coslovich D, Jack RL, Paret J. Dimensionality reduction of local structure in glassy binary mixtures. J Chem Phys 2022; 157:204503. [DOI: 10.1063/5.0128265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
We consider unsupervised learning methods for characterizing the disordered microscopic structure of supercooled liquids and glasses. Specifically, we perform dimensionality reduction of smooth structural descriptors that describe radial and bond-orientational correlations and assess the ability of the method to grasp the essential structural features of glassy binary mixtures. In several cases, a few collective variables account for the bulk of the structural fluctuations within the first coordination shell and also display a clear connection with the fluctuations of particle mobility. Fine-grained descriptors that characterize the radial dependence of bond-orientational order better capture the structural fluctuations relevant for particle mobility but are also more difficult to parameterize and to interpret. We also find that principal component analysis of bond-orientational order parameters provides identical results to neural network autoencoders while having the advantage of being easily interpretable. Overall, our results indicate that glassy binary mixtures have a broad spectrum of structural features. In the temperature range we investigate, some mixtures display well-defined locally favored structures, which are reflected in bimodal distributions of the structural variables identified by dimensionality reduction.
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Affiliation(s)
- Daniele Coslovich
- Dipartimento di Fisica, Università di Trieste, Strada Costiera 11, 34151 Trieste, Italy
| | - Robert L. Jack
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Joris Paret
- Laboratoire Charles Coulomb, Université de Montpellier, Montpellier, France
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29
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Lerbinger M, Barbot A, Vandembroucq D, Patinet S. Relevance of Shear Transformations in the Relaxation of Supercooled Liquids. PHYSICAL REVIEW LETTERS 2022; 129:195501. [PMID: 36399740 DOI: 10.1103/physrevlett.129.195501] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 07/18/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
While deeply supercooled liquids exhibit divergent viscosity and increasingly heterogeneous dynamics as the temperature drops, their structure shows only seemingly marginal changes. Understanding the nature of relaxation processes in this dramatic slowdown is key for understanding the glass transition. Here, we show by atomistic simulations that the heterogeneous dynamics of glass-forming liquids strongly correlate with the local residual plastic strengths along soft directions computed in the initial inherent structures. The correlation increases with decreasing temperature and is maximum in the vicinity of the relaxation time. For the lowest temperature investigated, this maximum is comparable with the best values from the literature dealing with the structure-property relationship. However, the nonlinear probe of the local shear resistance in soft directions provides here a real-space picture of relaxation processes. Our detection method of thermal rearrangements allows us to investigate the first passage time statistics and to study the scaling between the activation energy barriers and the residual plastic strengths. These results shed new light on the nature of relaxations of glassy systems by emphasizing the analogy between the thermal relaxations in viscous liquids and the plastic shear transformation in amorphous solids.
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Affiliation(s)
- Matthias Lerbinger
- PMMH, CNRS, ESPCI Paris, Université PSL, Sorbonne Université, Université Paris Cité, 75005 Paris, France
| | - Armand Barbot
- PMMH, CNRS, ESPCI Paris, Université PSL, Sorbonne Université, Université Paris Cité, 75005 Paris, France
| | - Damien Vandembroucq
- PMMH, CNRS, ESPCI Paris, Université PSL, Sorbonne Université, Université Paris Cité, 75005 Paris, France
| | - Sylvain Patinet
- PMMH, CNRS, ESPCI Paris, Université PSL, Sorbonne Université, Université Paris Cité, 75005 Paris, France
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30
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Luo C, Robinson JF, Pihlajamaa I, Debets VE, Royall CP, Janssen LMC. Many-Body Correlations Are Non-negligible in Both Fragile and Strong Glassformers. PHYSICAL REVIEW LETTERS 2022; 129:145501. [PMID: 36240416 DOI: 10.1103/physrevlett.129.145501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/29/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
It is widely believed that the emergence of slow glassy dynamics is encoded in a material's microstructure. First-principles theory [mode-coupling theory (MCT)] is able to predict the dramatic slowdown of the dynamics from only static two-point correlations as input, yet it cannot capture all of the observed dynamical behavior. Here we go beyond two-point spatial correlation functions by extending MCT systematically to include higher-order static and dynamic correlations. We demonstrate that only adding the static triplet direct correlations already qualitatively changes the predicted glass-transition diagram of binary hard spheres and silica. Moreover, we find a nontrivial competition between static triplet correlations that work to stabilize the glass state and dynamic higher-order correlations that destabilize it for both materials. We conclude that the conventionally neglected static triplet direct correlations as well as higher-order dynamic correlations are, in fact, non-negligible in both fragile and strong glassformers.
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Affiliation(s)
- Chengjie Luo
- Soft Matter and Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
| | - Joshua F Robinson
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 7-9, 55128 Mainz, Germany
- H. H. Wills Physics Laboratory, University of Bristol, Bristol BS8 1TL, United Kingdom
| | - Ilian Pihlajamaa
- Soft Matter and Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
| | - Vincent E Debets
- Soft Matter and Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
| | - C Patrick Royall
- H. H. Wills Physics Laboratory, University of Bristol, Bristol BS8 1TL, United Kingdom
- Gulliver UMR CNRS 7083, ESPCI Paris, Université PSL, 75005 Paris, France
- School of Chemistry, Cantock's Close, University of Bristol, Bristol BS8 1TS, United Kingdom
- Centre for Nanoscience and Quantum Information, University of Bristol, Bristol BS8 1FD, United Kingdom
| | - Liesbeth M C Janssen
- Soft Matter and Biological Physics, Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
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31
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Zhang K, Li X, Jin Y, Jiang Y. Machine learning glass caging order parameters with an artificial nested neural network. SOFT MATTER 2022; 18:6270-6277. [PMID: 35959881 DOI: 10.1039/d2sm00310d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Around a glass transition, the dynamics of a supercooled liquid dramatically slow down, exhibited by caging of particles, while the structural changes remain subtle. Alternative to recent machine learning studies searching for structural predictors of glassy dynamics, here we propose to learn directly particle caging features defined purely according to dynamics. We focus on three transitions in a simulated hard sphere glass model, the melting of ultra-stable glasses, the Gardner transition and the liquid to ordinary glass transition. Implementing the machine learning algorithm based on a two-level nested neural network, we attain not only appropriate caging order parameters for all three transitions, but also a phase classification for input samples. A finite-size scaling analysis of the phase classification results identifies the order of melting (first) and Gardner (second) transitions. A false positive is avoided, as the liquid to glass transition is indicated as a crossover, rather than a phase transition with a well-defined transition point. This study paves the way to a generic approach for learning dynamical features in glassy systems, with a minimum requirement of system-specific knowledge.
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Affiliation(s)
- Kaihua Zhang
- School of Chemistry, Beihang University, Beijing 100191, China.
| | - Xinyang Li
- CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuliang Jin
- CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325000, China
| | - Ying Jiang
- School of Chemistry, Beihang University, Beijing 100191, China.
- Center of Soft Matter Physics and Its Applications, Beihang University, Beijing 100191, China
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32
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Neural Networks Reveal the Impact of the Vibrational Dynamics in the Prediction of the Long-Time Mobility of Molecular Glassformers. Int J Mol Sci 2022; 23:ijms23169322. [PMID: 36012585 PMCID: PMC9409352 DOI: 10.3390/ijms23169322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
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 trained by information concerning the local structure of the environment surrounding a given particle. The only difference in the learning procedure is the inclusion (NN A) or not (NN B) of the information provided by the fast, vibrational dynamics and quantified by the local Debye–Waller factor. It is found that, for a given temperature, the prediction provided by the NN A is more accurate, a finding which is tentatively ascribed to better account of the bond reorientation. Both NNs are found to exhibit impressive and rather comparable performance to predict the four-point susceptibility χ4(t) at τα, a measure of the dynamic heterogeneity of the system.
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33
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Soltani S, Sinclair CW, Rottler J. Exploring glassy dynamics with Markov state models from graph dynamical neural networks. Phys Rev E 2022; 106:025308. [PMID: 36109953 DOI: 10.1103/physreve.106.025308] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
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 into a low-dimensional feature space. The transition timescale between states is larger than the conventional structural relaxation time τ_{α}, but can be obtained from trajectories much shorter than τ_{α}. The learned map of states assigned to the particles corresponds to local excess Voronoi volume. These results resonate with classic free volume theories of the glass transition, singling out local packing fluctuations as one of the dominant slowly relaxing features.
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Affiliation(s)
- Siavash Soltani
- Department of Materials Engineering, The University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
| | - Chad W Sinclair
- Department of Materials Engineering, The University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
| | - Jörg Rottler
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z1
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
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34
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Robust prediction of force chains in jammed solids using graph neural networks. Nat Commun 2022; 13:4424. [PMID: 35908018 PMCID: PMC9338954 DOI: 10.1038/s41467-022-31732-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
Force chains are quasi-linear self-organised structures carrying large stresses and are ubiquitous in jammed amorphous materials like granular materials, foams or even cell assemblies. Predicting where they will form upon deformation is crucial to describe the properties of such materials, but remains an open question. Here we demonstrate that graph neural networks (GNN) can accurately predict the location of force chains in both frictionless and frictional materials from the undeformed structure, without any additional information. The GNN prediction accuracy also proves to be robust to changes in packing fraction, mixture composition, amount of deformation, friction coefficient, system size, and the form of the interaction potential. By analysing the structure of the force chains, we identify the key features that affect prediction accuracy. Our results and methodology will be of interest for granular matter and disordered systems, e.g. in cases where direct force chain visualisation or force measurements are impossible.
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35
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Guiselin B, Tarjus G, Berthier L. Static self-induced heterogeneity in glass-forming liquids: Overlap as a microscope. J Chem Phys 2022; 156:194503. [PMID: 35597648 DOI: 10.1063/5.0086517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We propose and numerically implement a local probe of the static self-induced heterogeneity characterizing glass-forming liquids. This method relies on the equilibrium statistics of the overlap between pairs of configurations measured in mesoscopic cavities with unconstrained boundaries. By systematically changing the location of the probed cavity, we directly detect spatial variations of the overlap fluctuations. We provide a detailed analysis of the statistics of a local estimate of the configurational entropy, and we infer an estimate of the surface tension between amorphous states, ingredients that are both at the basis of the random first-order transition theory of glass formation. Our results represent the first direct attempt to visualize and quantify the self-induced heterogeneity underpinning the thermodynamics of glass formation. They pave the way for the development of coarse-grained effective theories and for a direct assessment of the role of thermodynamics in the activated dynamics of deeply supercooled liquids.
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Affiliation(s)
- Benjamin Guiselin
- Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, 34095 Montpellier, France
| | - Gilles Tarjus
- LPTMC, CNRS-UMR 7600, Sorbonne Université, 4 Pl. Jussieu, F-75005 Paris, France
| | - Ludovic Berthier
- Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, 34095 Montpellier, France
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36
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Alkemade RM, Boattini E, Filion L, Smallenburg F. Comparing machine learning techniques for predicting glassy dynamics. J Chem Phys 2022; 156:204503. [DOI: 10.1063/5.0088581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learning techniques, and increasingly sophisticated descriptors used to describe the environment around particles. In many cases, both the chosen machine learning technique and choice of structural descriptors are varied simultaneously, making it hard to quantitatively compare the performance of different machine learning approaches. Here, we use three different machine learning algorithms -- linear regression, neural networks, and GNNs -- to predict the dynamic propensity of a glassy binary hard-sphere mixture using as structural input a recursive set of order parameters recently introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we show, when these advanced descriptors are used, all three methods predict the dynamics with nearly equal accuracy. However, the linear regression is orders of magnitude faster to train making it by far the method of choice.
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Affiliation(s)
- Rinske M. Alkemade
- Utrecht University Debye Institute for Nanomaterial Science, Netherlands
| | | | - Laura Filion
- Physics, Utrecht University Debye Institute for Nanomaterial(s) Science, Netherlands
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37
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Kirchner KA, Cassar DR, Zanotto ED, Ono M, Kim SH, Doss K, Bødker ML, Smedskjaer MM, Kohara S, Tang L, Bauchy M, Wilkinson CJ, Yang Y, Welch RS, Mancini M, Mauro JC. Beyond the Average: Spatial and Temporal Fluctuations in Oxide Glass-Forming Systems. Chem Rev 2022; 123:1774-1840. [PMID: 35511603 DOI: 10.1021/acs.chemrev.1c00974] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Atomic structure dictates the performance of all materials systems; the characteristic of disordered materials is the significance of spatial and temporal fluctuations on composition-structure-property-performance relationships. Glass has a disordered atomic arrangement, which induces localized distributions in physical properties that are conventionally defined by average values. Quantifying these statistical distributions (including variances, fluctuations, and heterogeneities) is necessary to describe the complexity of glass-forming systems. Only recently have rigorous theories been developed to predict heterogeneities to manipulate and optimize glass properties. This article provides a comprehensive review of experimental, computational, and theoretical approaches to characterize and demonstrate the effects of short-, medium-, and long-range statistical fluctuations on physical properties (e.g., thermodynamic, kinetic, mechanical, and optical) and processes (e.g., relaxation, crystallization, and phase separation), focusing primarily on commercially relevant oxide glasses. Rigorous investigations of fluctuations enable researchers to improve the fundamental understanding of the chemistry and physics governing glass-forming systems and optimize structure-property-performance relationships for next-generation technological applications of glass, including damage-resistant electronic displays, safer pharmaceutical vials to store and transport vaccines, and lower-attenuation fiber optics. We invite the reader to join us in exploring what can be discovered by going beyond the average.
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Affiliation(s)
- Katelyn A Kirchner
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Daniel R Cassar
- Department of Materials Engineering, Federal University of São Carlos, São Carlos, Sao Paulo 13565-905, Brazil
- Ilum School of Science, Brazilian Center for Research in Energy and Materials, Campinas, Sao Paulo 13083-970, Brazil
| | - Edgar D Zanotto
- Department of Materials Engineering, Federal University of São Carlos, São Carlos, Sao Paulo 13565-905, Brazil
| | - Madoka Ono
- Research Institute for Electronic Science, Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- Materials Integration Laboratories, AGC Incorporated, Yokohama, Kanagawa 230-0045, Japan
| | - Seong H Kim
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Karan Doss
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Mikkel L Bødker
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Morten M Smedskjaer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Shinji Kohara
- Research Center for Advanced Measurement and Characterization National Institute for Materials Science, 1-2-1, Sengen, Tsukuba, Ibaraki 305-0047, Japan
| | - Longwen Tang
- Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States
| | - Mathieu Bauchy
- Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States
| | - Collin J Wilkinson
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Research and Development, GlassWRX, Beaufort, South Carolina 29906, United States
| | - Yongjian Yang
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Rebecca S Welch
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Matthew Mancini
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - John C Mauro
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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38
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Ridout SA, Rocks JW, Liu AJ. Correlation of plastic events with local structure in jammed packings across spatial dimensions. Proc Natl Acad Sci U S A 2022; 119:e2119006119. [PMID: 35412897 PMCID: PMC9169794 DOI: 10.1073/pnas.2119006119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/10/2022] [Indexed: 11/18/2022] Open
Abstract
In frictionless jammed packings, existing evidence suggests a picture in which localized physics dominates in low spatial dimensions, d = 2, 3, but quickly loses relevance as d rises, replaced by spatially extended mean-field behavior. For example, quasilocalized low-energy vibrational modes and low-coordination particles associated with deviation from mean-field behavior (rattlers and bucklers) all vanish rapidly with increasing d. These results suggest that localized rearrangements, which are associated with low-energy vibrational modes, correlated with local structure, and dominant in low dimensions, should give way in higher d to extended rearrangements uncorrelated with local structure. Here, we use machine learning to analyze simulations of jammed packings under athermal, quasistatic shear, identifying a local structural variable, softness, that correlates with rearrangements in dimensions d = 2 to d = 5. We find that softness—and even just the local coordination number Z—is essentially equally predictive of rearrangements in all d studied. This result provides direct evidence that local structure plays an important role in higher d, suggesting a modified picture for the dimensional cross-over to mean-field theory.
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Affiliation(s)
- Sean A. Ridout
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
| | - Jason W. Rocks
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
| | - Andrea J. Liu
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
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39
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Wang Y, Deng W, Huang Z, Li S. Descriptor-free unsupervised learning method for local structure identification in particle packings. J Chem Phys 2022; 156:154504. [DOI: 10.1063/5.0088056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Local structure identification is of great importance in many scientific and engineering fields. However, mathematical and supervised learning methods mostly rely on specific descriptors of local structure and can only be applied to particular packing configurations. In this work, we propose an improved unsupervised learning method, which is descriptor-free, for local structure identification in particle packing. The point cloud is used as the input of the improved method, which directly comes from spatial positions of particles and does not rely on specific descriptors. The improved method constructs an autoencoder based on the point cloud network combined with Gaussian mixture models for dimension reduction and clustering. Numerical examples show that the improved method performs well in local structure identification of quasicrystal disk and sphere packings, achieving comparable accuracy with previous methods. For disordered packings which have been considered nearly having no local structures, the improved method identifies a nontrivial 7-neighbor motif in the maximally dense random packing of disks, and finds acentric structural motifs in the random close packing of spheres, which demonstrate the ability on identification of new and unknown local structures. The improved unsupervised learning method would help mining information from massive simulation and experimental results, as well as devising new order parameters for particle packings.
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Affiliation(s)
| | - Wei Deng
- Peking University College of Engineering, China
| | | | - Shuixiang Li
- Mechanics and Aerospace Engineering, Peking University, China
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40
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Banerjee A, Sevilla M, Rudzinski JF, Cortes-Huerto R. Finite-size scaling and thermodynamics of model supercooled liquids: long-range concentration fluctuations and the role of attractive interactions. SOFT MATTER 2022; 18:2373-2382. [PMID: 35258066 DOI: 10.1039/d2sm00089j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We compute partial structure factors, Kirkwood-Buff integrals (KBIs) and chemical potentials of model supercooled liquids with and without attractive interactions. We aim at investigating whether relatively small differences in the tail of the radial distribution functions result in contrasting thermodynamic properties. Our results suggest that the attractive potential favours the nucleation of long-range structures. Indeed, upon decreasing temperature, Bathia-Thornton structure factors display anomalous behaviour in the k→0 limit. KBIs extrapolated to the thermodynamic limit confirm this picture, and excess coordination numbers identify the anomaly with long-range concentration fluctuations. By contrast, the purely repulsive system remains perfectly miscible for the same temperature interval and only reveals qualitatively similar concentration fluctuations in the crystalline state. Furthermore, differences in both isothermal compressibilities and chemical potentials show that thermodynamics is not entirely governed by the short-range repulsive part of the interaction potential, emphasising the nonperturbative role of attractive interactions. Finally, at higher density, where both systems display nearly identical dynamical properties and repulsive interactions become dominant, the anomaly disappears, and both systems also exhibit similar thermodynamic properties.
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Affiliation(s)
- Atreyee Banerjee
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany.
| | - Mauricio Sevilla
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany.
| | - Joseph F Rudzinski
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany.
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41
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Smallenburg F. Efficient event-driven simulations of hard spheres. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2022; 45:22. [PMID: 35274181 DOI: 10.1140/epje/s10189-022-00180-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
Hard spheres are arguably one of the most fundamental model systems in soft matter physics, and hence a common topic of simulation studies. Event-driven simulation methods provide an efficient method for studying the phase behavior and dynamics of hard spheres under a wide range of different conditions. Here, we examine the impact of several optimization strategies for speeding up event-driven molecular dynamics of hard spheres and present a light-weight simulation code that outperforms existing simulation codes over a large range of system sizes and packing fractions. The presented differences in simulation speed, typically a factor of five to ten, save significantly on both CPU time and energy consumption and may be a crucial factor for studying slow processes such as crystal nucleation and glassy dynamics.
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Affiliation(s)
- Frank Smallenburg
- Laboratoire de Physique des Solides, CNRS, Université Paris-Saclay, 91405, Orsay, France
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42
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Coslovich D, Ikeda A. Revisiting the single-saddle model for the β-relaxation of supercooled liquids. J Chem Phys 2022; 156:094503. [DOI: 10.1063/5.0083173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
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 principles. In this work, we revisit a simple theoretical model of the β-relaxation, i.e., the first step of the relaxation dynamics. The model, first introduced by Cavagna et al. [J. Phys. A: Math. Gen. 36, 10721 (2003)], describes the dynamics of the system in the neighborhood of a saddle point of the potential energy surface. We extend the model to account for density–density correlation functions and for the four-point dynamic susceptibility. We obtain analytical results for a simple schematic model, making contact with related results for p-spin models and with the predictions of inhomogeneous mode-coupling theory. Building on recent computational advances, we also explicitly compare the model predictions against overdamped Langevin dynamics simulations of a glass-forming liquid close to the mode-coupling crossover. The agreement is quantitative at the level of single-particle dynamic properties only up to the early β-regime. Due to its inherent harmonic approximation, however, the model is unable to predict the dynamics on the time scale relevant for structural relaxation. Nonetheless, our analysis suggests that the agreement with the simulations may be largely improved if the modes’ spatial localization is properly taken into account.
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Affiliation(s)
- Daniele Coslovich
- Dipartimento di Fisica, Università di Trieste, Strada Costiera 11, 34151 Trieste, Italy
| | - Atsushi Ikeda
- Graduate School of Arts and Science, University of Tokyo, Komaba, Tokyo 153-8902, Japan
- Research Center for Complex Systems Biology, Universal Biology Institute, University of Tokyo, Komaba, Tokyo 153-8902, Japan
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43
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Unsupervised topological learning approach of crystal nucleation. Sci Rep 2022; 12:3195. [PMID: 35210485 PMCID: PMC8873400 DOI: 10.1038/s41598-022-06963-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/27/2022] [Indexed: 12/12/2022] Open
Abstract
Nucleation phenomena commonly observed in our every day life are of fundamental, technological and societal importance in many areas, but some of their most intimate mechanisms remain however to be unravelled. Crystal nucleation, the early stages where the liquid-to-solid transition occurs upon undercooling, initiates at the atomic level on nanometre length and sub-picoseconds time scales and involves complex multidimensional mechanisms with local symmetry breaking that can hardly be observed experimentally in the very details. To reveal their structural features in simulations without a priori, an unsupervised learning approach founded on topological descriptors loaned from persistent homology concepts is proposed. Applied here to monatomic metals, it shows that both translational and orientational ordering always come into play simultaneously as a result of the strong bonding when homogeneous nucleation starts in regions with low five-fold symmetry. It also reveals the specificity of the nucleation pathways depending on the element considered, with features beyond the hypothesis of Classical Nucleation Theory.
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44
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Mitra S, Marín-Aguilar S, Sastry S, Smallenburg F, Foffi G. Correlation between plastic rearrangements and local structure in a cyclically driven glass. J Chem Phys 2022; 156:074503. [DOI: 10.1063/5.0077851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | | | - Srikanth Sastry
- Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, India
| | | | - Giuseppe Foffi
- Laboratoire de Physique des Solides, Laboratoire de Physique des Solides, France
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45
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Hernandes VF, Marques MS, Bordin JR. Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 34:024002. [PMID: 34638114 DOI: 10.1088/1361-648x/ac2f0f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
Characterization of phases of soft matter systems is a challenge faced in many physical chemical problems. For polymorphic fluids it is an even greater challenge. Specifically, glass forming fluids, as water, can have, besides solid polymorphism, more than one liquid and glassy phases, and even a liquid-liquid critical point. In this sense, we apply a neural network algorithm to analyze the phase behavior of a mixture of core-softened fluids that interact through the continuous-shouldered well (CSW) potential, which have liquid polymorphism and liquid-liquid critical points, similar to water. We also apply the neural network to mixtures of CSW fluids and core-softened alcohols models. We combine and expand methods based on bond-orientational order parameters to study mixtures, applied to mixtures of hardcore fluids and to supercooled water, to include longer range coordination shells. With this, the trained neural network was able to properly predict the crystalline solid phases, the fluid phases and the amorphous phase for the pure CSW and CSW-alcohols mixtures with high efficiency. More than this, information about the phase populations, obtained from the network approach, can help verify if the phase transition is continuous or discontinuous, and also to interpret how the metastable amorphous region spreads along the stable high density fluid phase. These findings help to understand the behavior of supercooled polymorphic fluids and extend the comprehension of how amphiphilic solutes affect the phases behavior.
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Affiliation(s)
- V F Hernandes
- Programa de Pós-Graduação em Física, Departamento de Física, Instituto de Física e Matemática, Universidade Federal de Pelotas, Caixa Postal 354, 96001-970, Pelotas-RS, Brazil
| | - M S Marques
- Centro das Ciências Exatas e das Tecnologias, Universidade Federal do Oeste da Bahia Rua Bertioga, 892, Morada Nobre, CEP 47810-059, Barreiras-BA, Brazil
| | - José Rafael Bordin
- Departamento de Física, Instituto de Física e Matemática, Universidade Federal de Pelotas, Caixa Postal 354, 96001-970, Pelotas-RS, Brazil
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46
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Frydrych K, Karimi K, Pecelerowicz M, Alvarez R, Dominguez-Gutiérrez FJ, Rovaris F, Papanikolaou S. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges. MATERIALS (BASEL, SWITZERLAND) 2021; 14:5764. [PMID: 34640157 PMCID: PMC8510221 DOI: 10.3390/ma14195764] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 11/23/2022]
Abstract
In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials' discovery or develop new understanding of materials' behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.
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Affiliation(s)
- Karol Frydrych
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Kamran Karimi
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Michal Pecelerowicz
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Rene Alvarez
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Francesco Javier Dominguez-Gutiérrez
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11749, USA
| | - Fabrizio Rovaris
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Stefanos Papanikolaou
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
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47
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Atamas N, Gavryushenko D, Yablochkova K, Lazarenko M, Taranyik G. Temperature and temporal heterogeneities of water dynamics in the physiological temperature range. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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48
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Boattini E, Smallenburg F, Filion L. Averaging Local Structure to Predict the Dynamic Propensity in Supercooled Liquids. PHYSICAL REVIEW LETTERS 2021; 127:088007. [PMID: 34477414 DOI: 10.1103/physrevlett.127.088007] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
Predicting the local dynamics of supercooled liquids based purely on local structure is a key challenge in our quest for understanding glassy materials. Recent years have seen an explosion of methods for making such a prediction, often via the application of increasingly complex machine learning techniques. The best predictions so far have involved so-called Graph Neural Networks (GNNs) whose accuracy comes at a cost of models that involve on the order of 10^{5} fit parameters. In this Letter, we propose 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. We demonstrate that this insight can be exploited to design a significantly more efficient model that provides essentially the same predictive power at a fraction of the computational complexity (approximately 1000 fit parameters), and demonstrate its success by fitting the dynamic propensity of Kob-Andersen and binary hard-sphere mixtures. We then use this to make predictions regarding the importance of radial and angular descriptors in the dynamics of both models.
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Affiliation(s)
- Emanuele Boattini
- Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, 3584CC Utrecht, Netherlands
| | - Frank Smallenburg
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405 Orsay, France
| | - Laura Filion
- Soft Condensed Matter, Debye Institute of Nanomaterials Science, Utrecht University, 3584CC Utrecht, Netherlands
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49
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Abstract
Machine learning is making a major impact in materials research. I review current progress across a selection of areas of ubiquitous soft matter. When applied to particle tracking, machine learning using convolution neural networks is providing impressive performance but there remain some significant problems to solve. Characterising ordered arrangements of particles is a huge challenge and machine learning has been deployed to create the description, perform the classification and tease out an interpretation using a wide array of techniques often with good success. In glass research, machine learning has proved decisive in quantifying very subtle correlations between the local structure around a site and the susceptibility towards a rearrangement event at that site. There are also beginning to be some impressive attempts to deploy machine learning in the design of composite soft materials. The discovery aspect of this new materials design meets the current interest in teaching algorithms to learn to extrapolate beyond the training data.
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Affiliation(s)
- Paul S Clegg
- School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK.
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