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

doi:10.1016/j.mechmat.2021.104156

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

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

18 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 language	English
Article number	104156
Journal	Mechanics of Materials
Volume	165
DOIs	https://doi.org/10.1016/j.mechmat.2021.104156
State	Published - Feb 2022

Bibliographical note

Funding Information:
The work described in Sections 4, 6 and 7 was sponsored by the Army Research Laboratory, United States and was accomplished under Cooperative Agreement Number W911NF-12-2-0022 . The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein The work described in Section 3 was sponsored by the Air Force Office of Scientific Research, United States under the MURI Award FA9550-16-1-0566 . The work described in Section 5 was sponsored by the Air Force Office of Scientific Research, United States through the Center of Excellence on High-Rate Deformation Physics of Heterogeneous Materials, Award FA9550-12-1-0091 and the Deutsche Forschungsgemeinschaft, Germany through the Sonderforschungsbereich 1060 ‘The mathematics of emergent effects’.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

Machine learning
Materials by design
Multiscale modeling
Uncertainty quantification

ASJC Scopus subject areas

Mechanics of Materials
Instrumentation
Materials Science (all)

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10.1016/j.mechmat.2021.104156

Cite this

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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.",

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note = "Funding Information: The work described in Sections 4, 6 and 7 was sponsored by the Army Research Laboratory, United States and was accomplished under Cooperative Agreement Number W911NF-12-2-0022 . The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein The work described in Section 3 was sponsored by the Air Force Office of Scientific Research, United States under the MURI Award FA9550-16-1-0566 . The work described in Section 5 was sponsored by the Air Force Office of Scientific Research, United States through the Center of Excellence on High-Rate Deformation Physics of Heterogeneous Materials, Award FA9550-12-1-0091 and the Deutsche Forschungsgemeinschaft, Germany through the Sonderforschungsbereich 1060 {\textquoteleft}The mathematics of emergent effects{\textquoteright}. Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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