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Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials.

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Title: Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials.
Authors: Wang, Rui1 (AUTHOR), Li, Jiaqing2 (AUTHOR) jiaqing@fzu.edu.cn
Source: Materials (1996-1944). Apr2026, Vol. 19 Issue 7, p1417. 5p.
Subjects: Multiple scale method, Metals, Finite element method, Computer simulation, Machine learning, Materials science, Artificial intelligence, Molecular dynamics
Abstract: This article focuses on recent advances in the multiscale characterization and computational modeling/simulation of metallic materials, emphasizing their importance for strategic sectors like aerospace and energy. It highlights the integration of experimental techniques across atomic, mesoscale, and macroscopic levels with computational methods, including molecular dynamics and finite element simulations, to better understand microstructural evolution and mechanical performance. The article also discusses the emerging role of artificial intelligence and machine learning in predicting material properties and accelerating materials development. Future directions include enhancing in situ characterization methods and combining physics-based models with data-driven approaches to improve the accuracy and efficiency of metallic materials research. [Extracted from the article]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 192958779
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Rui%22">Wang, Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiaqing%22">Li, Jiaqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jiaqing@fzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Apr2026, Vol. 19 Issue 7, p1417. 5p.
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  Data: <searchLink fieldCode="DE" term="%22Multiple+scale+method%22">Multiple scale method</searchLink><br /><searchLink fieldCode="DE" term="%22Metals%22">Metals</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+science%22">Materials science</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This article focuses on recent advances in the multiscale characterization and computational modeling/simulation of metallic materials, emphasizing their importance for strategic sectors like aerospace and energy. It highlights the integration of experimental techniques across atomic, mesoscale, and macroscopic levels with computational methods, including molecular dynamics and finite element simulations, to better understand microstructural evolution and mechanical performance. The article also discusses the emerging role of artificial intelligence and machine learning in predicting material properties and accelerating materials development. Future directions include enhancing in situ characterization methods and combining physics-based models with data-driven approaches to improve the accuracy and efficiency of metallic materials research. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192958779
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/ma19071417
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 5
        StartPage: 1417
    Subjects:
      – SubjectFull: Multiple scale method
        Type: general
      – SubjectFull: Metals
        Type: general
      – SubjectFull: Finite element method
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Materials science
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Molecular dynamics
        Type: general
    Titles:
      – TitleFull: Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials.
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          Name:
            NameFull: Wang, Rui
      – PersonEntity:
          Name:
            NameFull: Li, Jiaqing
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          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961944
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Materials (1996-1944)
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