Hierarchical Materials Informatics

Novel Analytics for Materials Data


  • Surya Kalidindi, Ph.D., George W. Woodruff School of Mechanical Engineering and the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

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 presents a major challenge because these linkages need to cover unimaginably large dimensional spaces. Materials Informatics demonstrates how it is now possible to automate the acquisition of fairly large experimental datasets and apply sophisticated numerical models on these datasets to examine their response during various processing (manufacturing) steps and/or service conditions, thereby helping to speed up the time needed for the design, development and deployment of important new materials.
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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.


Book information

  • Published: August 2015
  • ISBN: 978-0-12-410394-8

Table of Contents

Ch. 1. Materials, Data and Informatics
Ch. 2. Microstructure Function
Ch. 3. Spatial Correlations
Ch. 4. Reduced-Order Representations
Ch. 5. Generalized Composite Theories
Ch. 6. Structure-Property Linkages
Ch. 7. Process-Structure Linkages
Ch. 8. Emerging Data and Software Repositories
Ch. 9. e-Collaboration Platforms
Ch. 10. Future Directions, Needs and Challenges