{"id":"https://openalex.org/W4415232228","doi":"https://doi.org/10.48550/arxiv.2507.17980","title":"Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations","display_name":"Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations","publication_year":2025,"publication_date":"2025-07-23","ids":{"openalex":"https://openalex.org/W4415232228","doi":"https://doi.org/10.48550/arxiv.2507.17980"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.17980","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.17980","pdf_url":"https://arxiv.org/pdf/2507.17980","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.17980","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119146880","display_name":"Elyar Tourani","orcid":"https://orcid.org/0009-0003-5181-6835","observed_orcids":["https://orcid.org/0009-0003-5181-6835"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tourani, Elyar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041977617","display_name":"Brian Joseph Edwards","orcid":"https://orcid.org/0000-0002-2378-5627","observed_orcids":["https://orcid.org/0000-0002-2378-5627"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edwards, Brian J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5034482931","display_name":"Bamin Khomami","orcid":"https://orcid.org/0000-0002-0091-2312","observed_orcids":["https://orcid.org/0000-0002-0091-2312"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khomami, Bamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9560999870300293,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9560999870300293,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10389","display_name":"Polymer crystallization and properties","score":0.03530000150203705,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12044","display_name":"Advanced Physical and Chemical Molecular Interactions","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/polymer-crystallization","display_name":"polymer crystallization","score":0.9980000257492065},{"id":"https://openalex.org/keywords/crystallinity-index","display_name":"crystallinity index","score":0.8999999761581421},{"id":"https://openalex.org/keywords/order-parameter","display_name":"order parameter","score":0.9129999876022339},{"id":"https://openalex.org/keywords/crystallization-pathways","display_name":"crystallization pathways","score":0.7450000047683716},{"id":"https://openalex.org/keywords/atomistic-molecular-dynamics","display_name":"atomistic molecular dynamics","score":0.6539999842643738},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.38199999928474426},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.7070000171661377},{"id":"https://openalex.org/keywords/unsupervised-clustering","display_name":"unsupervised clustering","score":0.7459999918937683}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6121000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6111999750137329},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5906000137329102},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5224999785423279},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4097999930381775},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4018000066280365},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39309999346733093},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C59593255","wikidata":"https://www.wikidata.org/wiki/Q901663","display_name":"Molecular dynamics","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C46275449","wikidata":"https://www.wikidata.org/wiki/Q2458815","display_name":"Crystallinity","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C110530677","wikidata":"https://www.wikidata.org/wiki/Q1789148","display_name":"Crystallization of polymers","level":3,"score":0.37630000710487366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C203036418","wikidata":"https://www.wikidata.org/wiki/Q284256","display_name":"Crystallization","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C56052488","wikidata":"https://www.wikidata.org/wiki/Q103382","display_name":"Amorphous solid","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.28139999508857727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.27059999108314514},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.17980","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.17980","pdf_url":"https://arxiv.org/pdf/2507.17980","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2507.17980","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.17980","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.17980","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.17980","pdf_url":"https://arxiv.org/pdf/2507.17980","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Currently,":[0],"identification":[1],"of":[2,38,129,178,206,220],"crystallization":[3,159],"pathways":[4],"in":[5,60,251],"polymers":[6],"is":[7,54,70,154,170],"being":[8],"carried":[9],"out":[10],"using":[11,63,149],"molecular":[12,65],"simulation-based":[13],"data":[14],"on":[15,20,100,162,196],"a":[16,21,73,126,193,199,238,245],"preset":[17],"cut-off":[18],"point":[19],"single":[22],"order":[23,130,152],"parameter":[24],"(OP)":[25],"to":[26,35,56,89,124,142,156,247],"define":[27],"nucleated":[28],"or":[29,198],"crystallized":[30],"regions.":[31],"Aside":[32],"from":[33],"sensitivity":[34],"cut-off,":[36],"each":[37],"these":[39,163],"OPs":[40],"introduces":[41],"its":[42],"own":[43],"systematic":[44],"biases.":[45],"In":[46],"this":[47,136],"study,":[48],"an":[49],"integrated":[50],"machine":[51],"learning":[52,122],"workflow":[53,236],"presented":[55],"accurately":[57],"quantify":[58],"crystallinity":[59,167],"polymeric":[61],"systems":[62],"atomistic":[64],"dynamics":[66],"data.":[67],"Each":[68],"atom":[69],"represented":[71],"by":[72],"high-dimensional":[74],"feature":[75,145],"vector":[76],"that":[77,132,148,225],"combines":[78],"geometric,":[79],"thermodynamic-like,":[80],"and":[81,105,185,244],"symmetry-based":[82],"descriptors.":[83],"Low":[84],"dimensional":[85],"embeddings":[86,102],"are":[87],"employed":[88],"expose":[90],"latent":[91],"structural":[92,249],"fingerprints":[93],"within":[94],"atomic":[95],"environments.":[96],"Subsequently,":[97],"unsupervised":[98],"clustering":[99],"the":[101,144,158,166,173,183,212,223],"identified":[103],"crystalline":[104],"amorphous":[106],"atoms":[107],"with":[108,116],"high":[109,113],"fidelity.":[110],"After":[111],"generating":[112],"quality":[114],"labels":[115],"multidimensional":[117],"data,":[118],"we":[119,209],"use":[120],"supervised":[121],"techniques":[123],"identify":[125],"minimal":[127],"set":[128],"parameters":[131,153],"can":[133],"fully":[134],"capture":[135],"label.":[137],"Various":[138],"tests":[139],"were":[140],"conducted":[141],"reduce":[143],"set,":[146],"demonstrating":[147],"only":[150],"three":[151],"sufficient":[155],"recreate":[157],"labels.":[160],"Based":[161],"observed":[164],"OPs,":[165],"index":[168],"(C-index)":[169],"defined":[171],"as":[172],"logistic":[174],"regression":[175],"model's":[176],"probability":[177],"crystallinity,":[179],"remaining":[180],"bimodal":[181],"throughout":[182],"process":[184],"achieving":[186],"over":[187],"0.98":[188],"classification":[189],"performance":[190],"(AUC).":[191],"Notably,":[192],"model":[194],"trained":[195],"one":[197],"few":[200],"snapshots":[201],"enables":[202],"efficient":[203],"on-the-fly":[204],"computation":[205],"crystallinity.":[207],"Lastly,":[208],"demonstrate":[210],"how":[211],"optimal":[213],"C-index":[214],"fit":[215],"evolves":[216],"during":[217],"various":[218],"stages":[219],"crystallization,":[221],"supporting":[222],"hypothesis":[224],"entropy":[226],"dominates":[227],"early":[228],"nucleation,":[229],"while":[230],"symmetry":[231],"gains":[232],"relevance":[233],"later.":[234],"This":[235],"provides":[237],"data-driven":[239],"strategy":[240],"for":[241],"OP":[242],"selection":[243],"metric":[246],"monitor":[248],"transformations":[250],"large-scale":[252],"polymer":[253],"simulations.":[254]},"counts_by_year":[],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2025-10-16T00:00:00"}