Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques

Ramin Bostanabad, Yichi Zhang, Xiaolin Li, Tucker Kearney, L. Catherine Brinson, Daniel Apley, Wing K Liu, Wei Chen*

*Corresponding author for this work

Research output: Contribution to journalReview article

36 Citations (Scopus)

Abstract

Building sensible processing-structure-property (PSP) links to gain fundamental insights and understanding of materials behavior has been the focus of many works in computational materials science. Microstructure characterization and reconstruction (MCR), coupled with machine learning techniques and materials modeling and simulation, is an important component of discovering PSP relations and inverse material design in the era of high-throughput computational materials science. In this article, we provide a comprehensive review of representative approaches for MCR and elaborate on their algorithmic details, computational costs, and how they fit into the PSP mapping problems. Multiple categories of MCR methods relying on statistical functions (such as n-point correlation functions), physical descriptors, spectral density function, texture synthesis, and supervised/unsupervised learning are reviewed. As no MCR method is applicable to the analysis and (inverse) design of all material systems, our goal is to provide the scientific community with a close examination of the state-of-the-art techniques for MCR, as well as useful guidance on which MCR method to choose and how to systematically apply it to a problem at hand. We illustrate applications of MCR on materials modeling and building structure-property relations via two examples: One on learning the materials law of a class of composite microstructures, and the second on relating the permittivity and dielectric loss to a structural parameter in nanodielectrics.

Original languageEnglish (US)
Pages (from-to)1-41
Number of pages41
JournalProgress in Materials Science
Volume95
DOIs
StatePublished - Jun 1 2018

Fingerprint

Microstructure
Materials science
Processing
Unsupervised learning
Spectral density
Dielectric losses
Probability density function
Learning systems
Permittivity
Textures
Throughput
Composite materials
Costs

Keywords

  • Characterization and reconstruction
  • Computational materials design
  • Correlation functions
  • Microstructure
  • Processing-structure-property links
  • Spectral methods
  • Statistical equivalency
  • Supervised and unsupervised learning
  • Texture synthesis

ASJC Scopus subject areas

  • Materials Science(all)

Cite this

Bostanabad, Ramin ; Zhang, Yichi ; Li, Xiaolin ; Kearney, Tucker ; Brinson, L. Catherine ; Apley, Daniel ; Liu, Wing K ; Chen, Wei. / Computational microstructure characterization and reconstruction : Review of the state-of-the-art techniques. In: Progress in Materials Science. 2018 ; Vol. 95. pp. 1-41.
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Computational microstructure characterization and reconstruction : Review of the state-of-the-art techniques. / Bostanabad, Ramin; Zhang, Yichi; Li, Xiaolin; Kearney, Tucker; Brinson, L. Catherine; Apley, Daniel; Liu, Wing K; Chen, Wei.

In: Progress in Materials Science, Vol. 95, 01.06.2018, p. 1-41.

Research output: Contribution to journalReview article

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T1 - Computational microstructure characterization and reconstruction

T2 - Review of the state-of-the-art techniques

AU - Bostanabad, Ramin

AU - Zhang, Yichi

AU - Li, Xiaolin

AU - Kearney, Tucker

AU - Brinson, L. Catherine

AU - Apley, Daniel

AU - Liu, Wing K

AU - Chen, Wei

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AB - Building sensible processing-structure-property (PSP) links to gain fundamental insights and understanding of materials behavior has been the focus of many works in computational materials science. Microstructure characterization and reconstruction (MCR), coupled with machine learning techniques and materials modeling and simulation, is an important component of discovering PSP relations and inverse material design in the era of high-throughput computational materials science. In this article, we provide a comprehensive review of representative approaches for MCR and elaborate on their algorithmic details, computational costs, and how they fit into the PSP mapping problems. Multiple categories of MCR methods relying on statistical functions (such as n-point correlation functions), physical descriptors, spectral density function, texture synthesis, and supervised/unsupervised learning are reviewed. As no MCR method is applicable to the analysis and (inverse) design of all material systems, our goal is to provide the scientific community with a close examination of the state-of-the-art techniques for MCR, as well as useful guidance on which MCR method to choose and how to systematically apply it to a problem at hand. We illustrate applications of MCR on materials modeling and building structure-property relations via two examples: One on learning the materials law of a class of composite microstructures, and the second on relating the permittivity and dielectric loss to a structural parameter in nanodielectrics.

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