Machine learning workflow for analysis of high-dimensional order parameter space: A case study of polymer crystallization from molecular dynamics simulations.

Saved in:
Bibliographic Details
Title: Machine learning workflow for analysis of high-dimensional order parameter space: A case study of polymer crystallization from molecular dynamics simulations.
Authors: Tourani E; Materials Research and Innovation Laboratory, Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA., Edwards BJ; Materials Research and Innovation Laboratory, Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA., Khomami B; Materials Research and Innovation Laboratory, Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.
Source: The Journal of chemical physics [J Chem Phys] 2025 Oct 28; Vol. 163 (16).
Publication Type: Journal Article
Journal Info: Publisher: American Institute of Physics Country of Publication: United States NLM ID: 0375360 Publication Model: Print Cited Medium: Internet ISSN: 1089-7690 (Electronic) Linking ISSN: 00219606 NLM ISO Abbreviation: J Chem Phys Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 41171642
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine learning workflow for analysis of high-dimensional order parameter space: A case study of polymer crystallization from molecular dynamics simulations.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Tourani+E%22">Tourani E</searchLink>; Materials Research and Innovation Laboratory, Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.<br /><searchLink fieldCode="AU" term="%22Edwards+BJ%22">Edwards BJ</searchLink>; Materials Research and Innovation Laboratory, Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.<br /><searchLink fieldCode="AU" term="%22Khomami+B%22">Khomami B</searchLink>; Materials Research and Innovation Laboratory, Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%220375360%22">The Journal of chemical physics</searchLink> [J Chem Phys] 2025 Oct 28; Vol. 163 (16).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22American+Institute+of+Physics%22">American Institute of Physics </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>0375360 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1089-7690 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200219606%22">00219606 </searchLink><i>NLM ISO Abbreviation: </i>J Chem Phys <i>Subsets: </i>MEDLINE; PubMed not MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41171642
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1063/5.0292454
    Languages:
      – Code: eng
        Text: English
    Titles:
      – TitleFull: Machine learning workflow for analysis of high-dimensional order parameter space: A case study of polymer crystallization from molecular dynamics simulations.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tourani E
      – PersonEntity:
          Name:
            NameFull: Edwards BJ
      – PersonEntity:
          Name:
            NameFull: Khomami B
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 28
              M: 10
              Text: 2025 Oct 28
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 1089-7690
          Numbering:
            – Type: volume
              Value: 163
            – Type: issue
              Value: 16
          Titles:
            – TitleFull: The Journal of chemical physics
              Type: main
ResultId 1