The Resource Bayesian optimization for materials science, Daniel Packwood
Bayesian optimization for materials science, Daniel Packwood
Resource Information
The item Bayesian optimization for materials science, Daniel Packwood represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Massey University Library, University of New Zealand.This item is available to borrow from 1 library branch.
Resource Information
The item Bayesian optimization for materials science, Daniel Packwood represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Massey University Library, University of New Zealand.
This item is available to borrow from 1 library branch.
 Summary
 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 firstprinciples calculations to perform efficient, global structure optimizations. While research in these directions has been reported in highprofile 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
 Language
 eng
 Extent
 1 online resource
 Contents

 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
 Isbn
 9789811067815
 Label
 Bayesian optimization for materials science
 Title
 Bayesian optimization for materials science
 Statement of responsibility
 Daniel Packwood
 Language
 eng
 Summary
 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 firstprinciples calculations to perform efficient, global structure optimizations. While research in these directions has been reported in highprofile 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
 http://library.link/vocab/creatorName
 Packwood, Daniel
 Dewey number
 519.6
 Index
 no index present
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 SpringerBriefs in the Mathematics of Materials,
 Series volume
 3
 http://library.link/vocab/subjectName

 Mathematical optimization
 Materials
 Materials science
 Label
 Bayesian optimization for materials science, Daniel Packwood
 Bibliography note
 Includes bibliographical references
 Contents
 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
 Control code
 on1021273019
 Dimensions
 unknown
 Extent
 1 online resource
 Form of item
 online
 Isbn
 9789811067815
 Note
 SpringerLink
 Specific material designation
 remote
 System control number
 (OCoLC)1021273019
 Label
 Bayesian optimization for materials science, Daniel Packwood
 Bibliography note
 Includes bibliographical references
 Contents
 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
 Control code
 on1021273019
 Dimensions
 unknown
 Extent
 1 online resource
 Form of item
 online
 Isbn
 9789811067815
 Note
 SpringerLink
 Specific material designation
 remote
 System control number
 (OCoLC)1021273019
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