Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization

Nikola Kovachki, Burigede Liu, Xingsheng Sun, Hao Zhou, Kaushik Bhattacharya, Michael Ortiz, Andrew Stuart

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular application leads to the choice of a particular material system whose properties are optimized by manipulating its underlying microstructure through processing. The specific configuration of the structure is then designed by characterizing the material in detail, and using this characterization along with physical principles in system level simulations and optimization. These have been advanced by multiscale modeling of materials, high-throughput experimentations, materials data-bases, topology optimization and other ideas. Still, developing materials for extreme applications involving large deformation, high strain rates and high temperatures remains a challenge. This article reviews a number of recent methods that advance the goal of designing materials targeted by specific applications.

Original languageEnglish
Article number104156
JournalMechanics of Materials
Volume165
DOIs
StatePublished - Feb 2022

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Machine learning
  • Materials by design
  • Multiscale modeling
  • Uncertainty quantification

ASJC Scopus subject areas

  • Instrumentation
  • General Materials Science
  • Mechanics of Materials

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