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Bayesian Optimization for Materials Science

Bayesian Optimization for Materials Science
Catalogue Information
Field name Details
Dewey Class 620.11
Title Bayesian Optimization for Materials Science ([EBook]) / by Daniel Packwood.
Author Packwood, Daniel
Other name(s) SpringerLink (Online service)
Publication Singapore : Springer Singapore , 2017.
Physical Details VIII, 42 pages, 16 illus., 12 illus. in color. : online resource.
Series SpringerBriefs in the Mathematics of Materials 2365-6336 ; ; 3
ISBN 9789811067815
Summary Note 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.:
Contents note Chapter 1. Overview of Bayesian optimization in materials science -- Chapter 2. Theory of Bayesian optimization -- Chapter 3. Bayesian optimization of molecules adsorbed to metal surfaces.
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site http://dx.doi.org/10.1007/978-981-10-6781-5
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