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Springer Series in Materials Science

Materials Discovery and Design

By Means of Data Science and Optimal Learning

Editors: Lookman, T., Eidenbenz, S., Alexander, F., Barnes, C. (Eds.)

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  • Develops a new paradigm for using data science to guide materials discoveries
  • Describes information-theoretic tools and their application to materials science
  • Covers both analysis and processing of large scale computational and experimental data in materials science
  • With contributions from an interdisciplinary group of experts in the field
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eBook $84.99
price for USA in USD
  • ISBN 978-3-319-99465-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
Softcover $109.99
price for USA in USD
About this book

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample.  The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader. 

Table of contents (9 chapters)

Table of contents (9 chapters)
  • Dimensions, Bits, and Wows in Accelerating Materials Discovery

    Pages 1-14

    Varshney, Lav R.

  • Is Automated Materials Design and Discovery Possible?

    Pages 15-58

    McKerns, Michael

  • Importance of Feature Selection in Machine Learning and Adaptive Design for Materials

    Pages 59-79

    Balachandran, Prasanna V. (et al.)

  • Bayesian Approaches to Uncertainty Quantification and Structure Refinement from X-Ray Diffraction

    Pages 81-102

    Paterson, Alisa R. (et al.)

  • Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials

    Pages 103-128

    Ziatdinov, Maxim (et al.)

Buy this book

eBook $84.99
price for USA in USD
  • ISBN 978-3-319-99465-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
Softcover $109.99
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Materials Discovery and Design
Book Subtitle
By Means of Data Science and Optimal Learning
Editors
  • Turab Lookman
  • Stephan Eidenbenz
  • Frank Alexander
  • Cris Barnes
Series Title
Springer Series in Materials Science
Series Volume
280
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-319-99465-9
DOI
10.1007/978-3-319-99465-9
Hardcover ISBN
978-3-319-99464-2
Softcover ISBN
978-3-030-07602-3
Series ISSN
0933-033X
Edition Number
1
Number of Pages
XVI, 256
Number of Illustrations
10 b/w illustrations, 88 illustrations in colour
Topics