Structural changes during glass formation extracted by computational homology with machine learning View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-12-04

AUTHORS

Akihiko Hirata, Tomohide Wada, Ippei Obayashi, Yasuaki Hiraoka

ABSTRACT

The structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3–9 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states. More... »

PAGES

98

References to SciGraph publications

  • 2018-05-05. Persistence diagrams with linear machine learning models in JOURNAL OF APPLIED AND COMPUTATIONAL TOPOLOGY
  • 2002-11-01. Topological Persistence and Simplification in DISCRETE & COMPUTATIONAL GEOMETRY
  • 2001-03-08. Supercooled liquids and the glass transition in NATURE
  • 2016. Structural Analysis of Metallic Glasses with Computational Homology in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s43246-020-00100-3

    DOI

    http://dx.doi.org/10.1038/s43246-020-00100-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1133100892


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Mathematics for Advanced Materials-OIL, AIST, 980-8577, Sendai, Japan", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Department of Materials Science, Waseda University, 169-8555, Tokyo, Japan", 
                "Kagami Memorial Research Institute for Materials Science and Technology, Waseda University, 169-0051, Tokyo, Japan", 
                "WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan", 
                "Mathematics for Advanced Materials-OIL, AIST, 980-8577, Sendai, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hirata", 
            "givenName": "Akihiko", 
            "id": "sg:person.01054462015.95", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054462015.95"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan", 
              "id": "http://www.grid.ac/institutes/grid.69566.3a", 
              "name": [
                "WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wada", 
            "givenName": "Tomohide", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Kyoto University Institute for Advanced Study, WPI-ASHBi, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, 606-8501, Kyoto, Japan", 
              "id": "http://www.grid.ac/institutes/grid.258799.8", 
              "name": [
                "WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan", 
                "Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, 103-0027, Tokyo, Japan", 
                "Kyoto University Institute for Advanced Study, WPI-ASHBi, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, 606-8501, Kyoto, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Obayashi", 
            "givenName": "Ippei", 
            "id": "sg:person.012337042765.38", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012337042765.38"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Kyoto University Institute for Advanced Study, WPI-ASHBi, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, 606-8501, Kyoto, Japan", 
              "id": "http://www.grid.ac/institutes/grid.258799.8", 
              "name": [
                "Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, 103-0027, Tokyo, Japan", 
                "Kyoto University Institute for Advanced Study, WPI-ASHBi, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, 606-8501, Kyoto, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hiraoka", 
            "givenName": "Yasuaki", 
            "id": "sg:person.010323112461.69", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010323112461.69"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00454-002-2885-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001786843", 
              "https://doi.org/10.1007/s00454-002-2885-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-4-431-56056-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053693307", 
              "https://doi.org/10.1007/978-4-431-56056-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s41468-018-0013-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103830162", 
              "https://doi.org/10.1007/s41468-018-0013-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/35065704", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005355581", 
              "https://doi.org/10.1038/35065704"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2020-12-04", 
        "datePublishedReg": "2020-12-04", 
        "description": "The structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3\u20139 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states.", 
        "genre": "article", 
        "id": "sg:pub.10.1038/s43246-020-00100-3", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.9020844", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8425928", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.9021316", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.6819639", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.5917385", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.9185730", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.9023639", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.9189803", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1363526", 
            "issn": [
              "2662-4443"
            ], 
            "name": "Communications Materials", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "1"
          }
        ], 
        "keywords": [
          "atomic structure", 
          "atomic clusters", 
          "glass state", 
          "glass formation", 
          "local atomic structure", 
          "persistent homology method", 
          "structural origin", 
          "glass model", 
          "slow dynamics", 
          "metallic glass models", 
          "computational homology", 
          "static structure", 
          "linear machine", 
          "homology method", 
          "atoms", 
          "present method", 
          "drastic changes", 
          "state", 
          "structure", 
          "clusters", 
          "significant structural components", 
          "formation", 
          "structural changes", 
          "subtle structural differences", 
          "large number", 
          "dynamics", 
          "liquid", 
          "simulations", 
          "cooling rate", 
          "hierarchical features", 
          "machine", 
          "model", 
          "technique", 
          "origin", 
          "method", 
          "position", 
          "structural differences", 
          "number", 
          "components", 
          "features", 
          "different cooling rates", 
          "changes", 
          "structural components", 
          "rate", 
          "study", 
          "differences", 
          "homology"
        ], 
        "name": "Structural changes during glass formation extracted by computational homology with machine learning", 
        "pagination": "98", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1133100892"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s43246-020-00100-3"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s43246-020-00100-3", 
          "https://app.dimensions.ai/details/publication/pub.1133100892"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-09-02T16:06", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_858.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1038/s43246-020-00100-3"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s43246-020-00100-3'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s43246-020-00100-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s43246-020-00100-3'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s43246-020-00100-3'


     

    This table displays all metadata directly associated to this object as RDF triples.

    166 TRIPLES      21 PREDICATES      75 URIs      63 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s43246-020-00100-3 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N387b20f117584b40a4b82f833ba3ab82
    4 schema:citation sg:pub.10.1007/978-4-431-56056-2
    5 sg:pub.10.1007/s00454-002-2885-2
    6 sg:pub.10.1007/s41468-018-0013-5
    7 sg:pub.10.1038/35065704
    8 schema:datePublished 2020-12-04
    9 schema:datePublishedReg 2020-12-04
    10 schema:description The structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3–9 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states.
    11 schema:genre article
    12 schema:isAccessibleForFree true
    13 schema:isPartOf N64b9c7ecb8464f1b8f4263da87ce2efc
    14 N8b22e355404540c1b42c12a976a64024
    15 sg:journal.1363526
    16 schema:keywords atomic clusters
    17 atomic structure
    18 atoms
    19 changes
    20 clusters
    21 components
    22 computational homology
    23 cooling rate
    24 differences
    25 different cooling rates
    26 drastic changes
    27 dynamics
    28 features
    29 formation
    30 glass formation
    31 glass model
    32 glass state
    33 hierarchical features
    34 homology
    35 homology method
    36 large number
    37 linear machine
    38 liquid
    39 local atomic structure
    40 machine
    41 metallic glass models
    42 method
    43 model
    44 number
    45 origin
    46 persistent homology method
    47 position
    48 present method
    49 rate
    50 significant structural components
    51 simulations
    52 slow dynamics
    53 state
    54 static structure
    55 structural changes
    56 structural components
    57 structural differences
    58 structural origin
    59 structure
    60 study
    61 subtle structural differences
    62 technique
    63 schema:name Structural changes during glass formation extracted by computational homology with machine learning
    64 schema:pagination 98
    65 schema:productId N04d2540146364f5d963983e0fc6d8ebc
    66 N9423b37c510e4d56980b588505cada66
    67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1133100892
    68 https://doi.org/10.1038/s43246-020-00100-3
    69 schema:sdDatePublished 2022-09-02T16:06
    70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    71 schema:sdPublisher N4032b8ea05f6405c88899218c6dcc666
    72 schema:url https://doi.org/10.1038/s43246-020-00100-3
    73 sgo:license sg:explorer/license/
    74 sgo:sdDataset articles
    75 rdf:type schema:ScholarlyArticle
    76 N04d2540146364f5d963983e0fc6d8ebc schema:name dimensions_id
    77 schema:value pub.1133100892
    78 rdf:type schema:PropertyValue
    79 N1036b5348a6f4c1182dfd28ac940adf9 rdf:first sg:person.012337042765.38
    80 rdf:rest Nc729b90b7d2d45a39ef07f7a25b01e4b
    81 N387b20f117584b40a4b82f833ba3ab82 rdf:first sg:person.01054462015.95
    82 rdf:rest Neca16c44afb2492b965dffea997d4e33
    83 N4032b8ea05f6405c88899218c6dcc666 schema:name Springer Nature - SN SciGraph project
    84 rdf:type schema:Organization
    85 N64b9c7ecb8464f1b8f4263da87ce2efc schema:issueNumber 1
    86 rdf:type schema:PublicationIssue
    87 N720ef13fbea94c3d9af3c3de7807f66d schema:affiliation grid-institutes:grid.69566.3a
    88 schema:familyName Wada
    89 schema:givenName Tomohide
    90 rdf:type schema:Person
    91 N8b22e355404540c1b42c12a976a64024 schema:volumeNumber 1
    92 rdf:type schema:PublicationVolume
    93 N9423b37c510e4d56980b588505cada66 schema:name doi
    94 schema:value 10.1038/s43246-020-00100-3
    95 rdf:type schema:PropertyValue
    96 Nc729b90b7d2d45a39ef07f7a25b01e4b rdf:first sg:person.010323112461.69
    97 rdf:rest rdf:nil
    98 Neca16c44afb2492b965dffea997d4e33 rdf:first N720ef13fbea94c3d9af3c3de7807f66d
    99 rdf:rest N1036b5348a6f4c1182dfd28ac940adf9
    100 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Information and Computing Sciences
    102 rdf:type schema:DefinedTerm
    103 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    104 schema:name Artificial Intelligence and Image Processing
    105 rdf:type schema:DefinedTerm
    106 sg:grant.5917385 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    107 rdf:type schema:MonetaryGrant
    108 sg:grant.6819639 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    109 rdf:type schema:MonetaryGrant
    110 sg:grant.8425928 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    111 rdf:type schema:MonetaryGrant
    112 sg:grant.9020844 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    113 rdf:type schema:MonetaryGrant
    114 sg:grant.9021316 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    115 rdf:type schema:MonetaryGrant
    116 sg:grant.9023639 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    117 rdf:type schema:MonetaryGrant
    118 sg:grant.9185730 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    119 rdf:type schema:MonetaryGrant
    120 sg:grant.9189803 http://pending.schema.org/fundedItem sg:pub.10.1038/s43246-020-00100-3
    121 rdf:type schema:MonetaryGrant
    122 sg:journal.1363526 schema:issn 2662-4443
    123 schema:name Communications Materials
    124 schema:publisher Springer Nature
    125 rdf:type schema:Periodical
    126 sg:person.010323112461.69 schema:affiliation grid-institutes:grid.258799.8
    127 schema:familyName Hiraoka
    128 schema:givenName Yasuaki
    129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010323112461.69
    130 rdf:type schema:Person
    131 sg:person.01054462015.95 schema:affiliation grid-institutes:None
    132 schema:familyName Hirata
    133 schema:givenName Akihiko
    134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054462015.95
    135 rdf:type schema:Person
    136 sg:person.012337042765.38 schema:affiliation grid-institutes:grid.258799.8
    137 schema:familyName Obayashi
    138 schema:givenName Ippei
    139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012337042765.38
    140 rdf:type schema:Person
    141 sg:pub.10.1007/978-4-431-56056-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053693307
    142 https://doi.org/10.1007/978-4-431-56056-2
    143 rdf:type schema:CreativeWork
    144 sg:pub.10.1007/s00454-002-2885-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001786843
    145 https://doi.org/10.1007/s00454-002-2885-2
    146 rdf:type schema:CreativeWork
    147 sg:pub.10.1007/s41468-018-0013-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103830162
    148 https://doi.org/10.1007/s41468-018-0013-5
    149 rdf:type schema:CreativeWork
    150 sg:pub.10.1038/35065704 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005355581
    151 https://doi.org/10.1038/35065704
    152 rdf:type schema:CreativeWork
    153 grid-institutes:None schema:alternateName Mathematics for Advanced Materials-OIL, AIST, 980-8577, Sendai, Japan
    154 schema:name Department of Materials Science, Waseda University, 169-8555, Tokyo, Japan
    155 Kagami Memorial Research Institute for Materials Science and Technology, Waseda University, 169-0051, Tokyo, Japan
    156 Mathematics for Advanced Materials-OIL, AIST, 980-8577, Sendai, Japan
    157 WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan
    158 rdf:type schema:Organization
    159 grid-institutes:grid.258799.8 schema:alternateName Kyoto University Institute for Advanced Study, WPI-ASHBi, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, 606-8501, Kyoto, Japan
    160 schema:name Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, 103-0027, Tokyo, Japan
    161 Kyoto University Institute for Advanced Study, WPI-ASHBi, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, 606-8501, Kyoto, Japan
    162 WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan
    163 rdf:type schema:Organization
    164 grid-institutes:grid.69566.3a schema:alternateName WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan
    165 schema:name WPI Advanced Institute for Materials Research, Tohoku University, 980-8577, Sendai, Japan
    166 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...