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Bayesian optimization for materials ...

Packwood, Daniel.

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Bayesian optimization for materials science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian optimization for materials science/ by Daniel Packwood.
Author:	Packwood, Daniel.
Published:	Singapore :Springer Singapore : : 2017.,
Description:	viii, 42 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Mathematical optimization. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-6781-5
ISBN:	9789811067815

Bayesian optimization for materials science
Packwood, Daniel.

Bayesian optimization for materials science[electronic resource] /by Daniel Packwood. - Singapore :Springer Singapore :2017. - viii, 42 p. :ill., digital ;24 cm. - SpringerBriefs in the mathematics of materials,v.32365-6336 ;. - SpringerBriefs in the mathematics of materials ;v.3..

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.

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.

ISBN: 9789811067815

Standard No.: 10.1007/978-981-10-6781-5doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Bayesian optimization for materials science
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505 0 $a 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.
520 $a 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.
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