Hierarchical Materials Informatics

Hierarchical Materials Informatics

Novel Analytics for Materials Data

1st Edition - August 6, 2015

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  • Author: Surya R. Kalidindi
  • Hardcover ISBN: 9780124103948
  • eBook ISBN: 9780124104556

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Custom design, manufacture, and deployment of new high performance materials for advanced technologies is critically dependent on the availability of invertible, high fidelity, structure-property-processing (SPP) linkages. Establishing these linkages presents a major challenge because of the need to cover unimaginably large dimensional spaces. Hierarchical Materials Informatics addresses objective, computationally efficient, mining of large ensembles of experimental and modeling datasets to extract this core materials knowledge. Furthermore, it aims to organize and present this high value knowledge in highly accessible forms to end users engaged in product design and design for manufacturing efforts. As such, this emerging field has a pivotal role in realizing the goals outlined in current strategic national initiatives such as the Materials Genome Initiative (MGI) and the Advanced Manufacturing Partnership (AMP). This book presents the foundational elements of this new discipline as it relates to the design, development, and deployment of hierarchical materials critical to advanced technologies.

Key Features

  • Addresses a critical gap in new materials research and development by presenting a rigorous statistical framework for the quantification of microstructure
  • Contains several case studies illustrating the use of modern data analytic tools on microstructure datasets (both experimental and modeling)


Materials scientists and engineers and mechanical engineers and researchers across academia, government and industry who are working in the area of new materials design, development and deployment; graduate students in materials science and engineering.

Table of Contents

    • Acknowledgments
    • 1. Materials, Data, and Informatics
      • 1.1 PSP Linkages
      • 1.2 Material Internal Structure
      • 1.3 Inverse Problems in Materials and Process Design
      • 1.4 Data, Information, Knowledge, and Wisdom
      • 1.5 Digital Representations
      • 1.6 Hierarchical Materials Informatics
      • References
    • 2. Microstructure Function
      • 2.1 Length Scales
      • 2.2 Local States and Local State Spaces
      • 2.3 Microstructure Function
      • 2.4 Digital Representation of Functions
      • 2.5 Digital Representation of Microstructure Function
      • 2.6 Spectral Representations of Microstructure Function
      • References
    • 3. Statistical Quantification of Material Structure
      • 3.1 Spatial Correlations
      • 3.2 Computation and Visualization of 2-Point Spatial Correlations
      • 3.3 Higher Order Spatial Correlations
      • 3.4 Reconstructions of Microstructures from Spatial Correlations
      • 3.5 Reconstructions from Partial Sets of 2-Point Statistics
      • 3.6 Representative Microstructures
      • References
    • 4. Reduced-Order Representations of Spatial Correlations
      • 4.1 Principal Component Analyses
      • 4.2 Application to Spatial Correlations
      • 4.3 Case Study: α−β Ti Micrographs
      • 4.4 Case Study: Nonmetallic Inclusions/Steel Composite System
      • 4.5 Case Study: MD Simulation Datasets
      • References
    • 5. Generalized Composite Theories
      • 5.1 Conventions and Notations
      • 5.2 Review of Continuum Mechanics
      • 5.3 Concept of Homogenization
      • 5.4 Higher Order Homogenization Theory
      • References
    • 6. Structure–Property Linkages
      • 6.1 Data-Driven Framework for Homogenization Linkages
      • 6.2 Main Steps of the Data-Driven Framework for Homogenization Linkages
      • 6.3 Case Study: Microstructure–Property Relationships in Porous Transport Layers
      • 6.4 Case Study: Structure-Property Linkages in Inclusions/Steel Composites
      • 6.5 MKS: Data-Driven Framework for Localization Linkages
      • 6.6 Case Study: MKS for Elastic Response of Composites
      • 6.7 Case Study: MKS for Elastic Response of Higher Contrast Composites
      • 6.8 Case Study: MKS for Elastic Response of Polycrystals
      • 6.9 Case Study: MKS for Perfectly Plastic Response of Composites
      • References
    • 7. Process–Structure Linkages
      • 7.1 Mathematical Framework
      • 7.2 Case Study: Microstructure Evolution Using Phase-Field Models
      • 7.3 Case Study: DFT Databases for Crystal Plasticity Computations
      • References
    • 8. Materials Innovation Cyberinfrastructure
      • References
    • Index

Product details

  • No. of pages: 230
  • Language: English
  • Copyright: © Butterworth-Heinemann 2015
  • Published: August 6, 2015
  • Imprint: Butterworth-Heinemann
  • Hardcover ISBN: 9780124103948
  • eBook ISBN: 9780124104556

About the Author

Surya R. Kalidindi

Surya R. Kalidindi
Surya R. Kalidindi earned a B.Tech. in Civil Engineering from the Indian Institute of Technology, Madras, an M.S. in Civil Engineering from Case Western Reserve University, and a Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology. After his graduation from MIT in 1992, Surya joined the Department of Materials Science and Engineering at Drexel University as an Assistant Professor, where he served as the Department Head during 2000-2008. In 2013, Surya accepted a new position as a Professor of Mechanical Engineering in the George W. Woodruff School at Georgia Institute of Technology, with joint appointments in the School of Computational Science and Engineering and in the School of Materials Science and Engineering. Surya’s research efforts over the past two decades have made seminal contributions to the fields of crystal plasticity, microstructure design, spherical nanoindentation, and materials informatics. His work has already produced about 200 journal articles, four book chapters, and a new book on Microstructure Sensitive Design. His work is well cited by peer researchers as reflected by an h-index of 52 and current citation rate of about 1000 citations/year (Google Scholar). He has recently been awarded the Alexander von Humboldt award in recognition of his lifetime achievements in research. He has been elected a Fellow of ASME, ASM International, TMS, and Alpha Sigma Mu.

Affiliations and Expertise

George W. Woodruff School of Mechanical Engineering and the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

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