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

TL;DR

This paper reviews recent multiscale modeling and data-driven methods that accelerate the design of materials tailored for specific applications, especially under extreme conditions involving large deformation and high temperatures.

Contribution

It provides a comprehensive overview of recent advances in multiscale modeling, data science, and optimization techniques for targeted material design.

Findings

Advances in multiscale modeling improve material property predictions.

Integration of high-throughput experiments accelerates material discovery.

Optimization methods enable tailored microstructure design for extreme conditions.

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.