Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in the Mathematics of Materials (BRIEFSMAMA, volume 3)
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Table of contents (3 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Bayesian Optimization for Materials Science
Authors: Daniel Packwood
Series Title: SpringerBriefs in the Mathematics of Materials
DOI: https://doi.org/10.1007/978-981-10-6781-5
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-981-10-6780-8Published: 12 October 2017
eBook ISBN: 978-981-10-6781-5Published: 04 October 2017
Series ISSN: 2365-6336
Series E-ISSN: 2365-6344
Edition Number: 1
Number of Pages: VIII, 42
Number of Illustrations: 4 b/w illustrations, 12 illustrations in colour
Topics: Energy Materials, Statistical Theory and Methods, Statistical Physics and Dynamical Systems