Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization. (February 2022)

• Record Type: Journal Article
• Authors: Kovachki, Nikola; Liu, Burigede; Sun, Xingsheng; Zhou, Hao; Bhattacharya, Kaushik; Ortiz, Michael; Stuart, Andrew
• Is Part Of: Mechanics of materials. Volume 165(2022)
• Publication Date: 2022-02
• Subjects: Multiscale modeling -- Materials by design -- Machine learning -- Uncertainty quantification; Strength of materials -- Periodicals; Mechanics, Applied -- Periodicals; Résistance des matériaux -- Périodiques; Mécanique appliquée -- Périodiques; Mechanics, Applied; Strength of materials; Periodicals; Electronic journals; 620.11
• DOI: 10.1016/j.mechmat.2021.104156
• Languages: English
• ISSNs: 0167-6636

Abstract

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. Highlights: Review of a number of recent methods that advance the goal of designing materials targeted by specific applications.