SpringerBriefs in the Mathematics of Materials

Bayesian Optimization for Materials Science

Authors: Packwood, Daniel

  • Is a timely publication as Bayesian optimization gains interest in materials science, and is one of the few introductions to this method for materials scientists
  • Makes the mathematical content appealing to materials scientists with its interesting application to structure optimization problems
  • Enables materials scientists to use Bayesian optimization in their own research
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eBook 41,64 €
price for Spain (gross)
  • ISBN 978-981-10-6781-5
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 51,99 €
price for Spain (gross)
  • ISBN 978-981-10-6780-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science.Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While research in these directions has been reported in high-profile journals, until now there has been no textbook aimed specifically at materials scientists who wish to incorporate Bayesian optimization into their own research. This book will be accessible to researchers and students in materials science who have a basic background in calculus and linear algebra.

Table of contents (3 chapters)

  • Overview of Bayesian Optimization in Materials Science

    Packwood, Daniel

    Pages 1-10

    Preview Buy Chapter 30,19 €
  • Theory of Bayesian Optimization

    Packwood, Daniel

    Pages 11-28

    Preview Buy Chapter 30,19 €
  • Bayesian Optimization of Molecules Adsorbed to Metal Surfaces

    Packwood, Daniel

    Pages 29-42

    Preview Buy Chapter 30,19 €

Buy this book

eBook 41,64 €
price for Spain (gross)
  • ISBN 978-981-10-6781-5
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 51,99 €
price for Spain (gross)
  • ISBN 978-981-10-6780-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Bayesian Optimization for Materials Science
Authors
Series Title
SpringerBriefs in the Mathematics of Materials
Series Volume
3
Copyright
2017
Publisher
Springer Singapore
Copyright Holder
The Author(s)
eBook ISBN
978-981-10-6781-5
DOI
10.1007/978-981-10-6781-5
Softcover ISBN
978-981-10-6780-8
Series ISSN
2365-6336
Edition Number
1
Number of Pages
VIII, 42
Number of Illustrations and Tables
4 b/w illustrations, 12 illustrations in colour
Topics