Identifying structural flow defects in disordered solids using machine-learning methods
We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a...
Gespeichert in:
| Veröffentlicht in: | Physical review letters 2015-03, Vol.114 (10), p.108001-108001, Article 108001 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 108001 |
|---|---|
| container_issue | 10 |
| container_start_page | 108001 |
| container_title | Physical review letters |
| container_volume | 114 |
| creator | Cubuk, E D Schoenholz, S S Rieser, J M Malone, B D Rottler, J Durian, D J Kaxiras, E Liu, A J |
| description | We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials. |
| doi_str_mv | 10.1103/physrevlett.114.108001 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_osti_</sourceid><recordid>TN_cdi_osti_scitechconnect_1180061</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1667958290</sourcerecordid><originalsourceid>FETCH-LOGICAL-c538t-8356a6a3aae222fe03530fe5e090c339427627345e1c7bca39051d52b596903c3</originalsourceid><addsrcrecordid>eNqFkU2LFDEQhoMo7jj6F5bGk5deK8kk6Rxl8WNhYEUUjyGTVDuRns6YSq_Mvzfr7Hr1FHh53lRRD2OXHK44B_n2uD9RwbsJa23B5orDAMCfsBUHY3vToqdsBSB5bwHMBXtB9BMaIfTwnF0INXBltVmx7zcR55rGU5p_dFTLEupS_NSNU_7dRRwxVOrS3MVEuUQsGDvKU4rULXRfOfiwTzP2E_oy_w2w7nOkl-zZ6CfCVw_vmn378P7r9ad-e_vx5vrdtg9KDrUfpNJee-k9CiFGBKkkjKgQLAQp7UYYLYzcKOTB7IKXFhSPSuza9hZkkGv2-vxvppochVQx7EOe57a447wdRfMGvTlDx5J_LUjVHRIFnCY_Y17IcTNobgdjzP9RrY1Vg2jD10yf0VAyNRmjO5Z08OXkOLh7Se5zk_QF77ZNUgs27iypFS8fZiy7A8Z_tUcr8g82npBQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1667958290</pqid></control><display><type>article</type><title>Identifying structural flow defects in disordered solids using machine-learning methods</title><source>APS: American Physical Society E-Journals (Physics)</source><creator>Cubuk, E D ; Schoenholz, S S ; Rieser, J M ; Malone, B D ; Rottler, J ; Durian, D J ; Kaxiras, E ; Liu, A J</creator><creatorcontrib>Cubuk, E D ; Schoenholz, S S ; Rieser, J M ; Malone, B D ; Rottler, J ; Durian, D J ; Kaxiras, E ; Liu, A J</creatorcontrib><description>We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.</description><identifier>ISSN: 0031-9007</identifier><identifier>EISSN: 1079-7114</identifier><identifier>DOI: 10.1103/physrevlett.114.108001</identifier><identifier>PMID: 25815967</identifier><language>eng</language><publisher>United States: American Physical Society</publisher><subject>Compressing ; Defects ; Dynamics ; Glass transition temperature ; Pillars ; Rest ; Three dimensional ; Two dimensional</subject><ispartof>Physical review letters, 2015-03, Vol.114 (10), p.108001-108001, Article 108001</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c538t-8356a6a3aae222fe03530fe5e090c339427627345e1c7bca39051d52b596903c3</citedby><cites>FETCH-LOGICAL-c538t-8356a6a3aae222fe03530fe5e090c339427627345e1c7bca39051d52b596903c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,2863,2864,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25815967$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/1180061$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Cubuk, E D</creatorcontrib><creatorcontrib>Schoenholz, S S</creatorcontrib><creatorcontrib>Rieser, J M</creatorcontrib><creatorcontrib>Malone, B D</creatorcontrib><creatorcontrib>Rottler, J</creatorcontrib><creatorcontrib>Durian, D J</creatorcontrib><creatorcontrib>Kaxiras, E</creatorcontrib><creatorcontrib>Liu, A J</creatorcontrib><title>Identifying structural flow defects in disordered solids using machine-learning methods</title><title>Physical review letters</title><addtitle>Phys Rev Lett</addtitle><description>We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.</description><subject>Compressing</subject><subject>Defects</subject><subject>Dynamics</subject><subject>Glass transition temperature</subject><subject>Pillars</subject><subject>Rest</subject><subject>Three dimensional</subject><subject>Two dimensional</subject><issn>0031-9007</issn><issn>1079-7114</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFkU2LFDEQhoMo7jj6F5bGk5deK8kk6Rxl8WNhYEUUjyGTVDuRns6YSq_Mvzfr7Hr1FHh53lRRD2OXHK44B_n2uD9RwbsJa23B5orDAMCfsBUHY3vToqdsBSB5bwHMBXtB9BMaIfTwnF0INXBltVmx7zcR55rGU5p_dFTLEupS_NSNU_7dRRwxVOrS3MVEuUQsGDvKU4rULXRfOfiwTzP2E_oy_w2w7nOkl-zZ6CfCVw_vmn378P7r9ad-e_vx5vrdtg9KDrUfpNJee-k9CiFGBKkkjKgQLAQp7UYYLYzcKOTB7IKXFhSPSuza9hZkkGv2-vxvppochVQx7EOe57a447wdRfMGvTlDx5J_LUjVHRIFnCY_Y17IcTNobgdjzP9RrY1Vg2jD10yf0VAyNRmjO5Z08OXkOLh7Se5zk_QF77ZNUgs27iypFS8fZiy7A8Z_tUcr8g82npBQ</recordid><startdate>20150309</startdate><enddate>20150309</enddate><creator>Cubuk, E D</creator><creator>Schoenholz, S S</creator><creator>Rieser, J M</creator><creator>Malone, B D</creator><creator>Rottler, J</creator><creator>Durian, D J</creator><creator>Kaxiras, E</creator><creator>Liu, A J</creator><general>American Physical Society</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>OTOTI</scope></search><sort><creationdate>20150309</creationdate><title>Identifying structural flow defects in disordered solids using machine-learning methods</title><author>Cubuk, E D ; Schoenholz, S S ; Rieser, J M ; Malone, B D ; Rottler, J ; Durian, D J ; Kaxiras, E ; Liu, A J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c538t-8356a6a3aae222fe03530fe5e090c339427627345e1c7bca39051d52b596903c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Compressing</topic><topic>Defects</topic><topic>Dynamics</topic><topic>Glass transition temperature</topic><topic>Pillars</topic><topic>Rest</topic><topic>Three dimensional</topic><topic>Two dimensional</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cubuk, E D</creatorcontrib><creatorcontrib>Schoenholz, S S</creatorcontrib><creatorcontrib>Rieser, J M</creatorcontrib><creatorcontrib>Malone, B D</creatorcontrib><creatorcontrib>Rottler, J</creatorcontrib><creatorcontrib>Durian, D J</creatorcontrib><creatorcontrib>Kaxiras, E</creatorcontrib><creatorcontrib>Liu, A J</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>OSTI.GOV</collection><jtitle>Physical review letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cubuk, E D</au><au>Schoenholz, S S</au><au>Rieser, J M</au><au>Malone, B D</au><au>Rottler, J</au><au>Durian, D J</au><au>Kaxiras, E</au><au>Liu, A J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying structural flow defects in disordered solids using machine-learning methods</atitle><jtitle>Physical review letters</jtitle><addtitle>Phys Rev Lett</addtitle><date>2015-03-09</date><risdate>2015</risdate><volume>114</volume><issue>10</issue><spage>108001</spage><epage>108001</epage><pages>108001-108001</pages><artnum>108001</artnum><issn>0031-9007</issn><eissn>1079-7114</eissn><abstract>We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.</abstract><cop>United States</cop><pub>American Physical Society</pub><pmid>25815967</pmid><doi>10.1103/physrevlett.114.108001</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0031-9007 |
| ispartof | Physical review letters, 2015-03, Vol.114 (10), p.108001-108001, Article 108001 |
| issn | 0031-9007 1079-7114 |
| language | eng |
| recordid | cdi_osti_scitechconnect_1180061 |
| source | APS: American Physical Society E-Journals (Physics) |
| subjects | Compressing Defects Dynamics Glass transition temperature Pillars Rest Three dimensional Two dimensional |
| title | Identifying structural flow defects in disordered solids using machine-learning methods |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T17%3A51%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_osti_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identifying%20structural%20flow%20defects%20in%20disordered%20solids%20using%20machine-learning%20methods&rft.jtitle=Physical%20review%20letters&rft.au=Cubuk,%20E%20D&rft.date=2015-03-09&rft.volume=114&rft.issue=10&rft.spage=108001&rft.epage=108001&rft.pages=108001-108001&rft.artnum=108001&rft.issn=0031-9007&rft.eissn=1079-7114&rft_id=info:doi/10.1103/physrevlett.114.108001&rft_dat=%3Cproquest_osti_%3E1667958290%3C/proquest_osti_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1667958290&rft_id=info:pmid/25815967&rfr_iscdi=true |