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171103 ||| eng |
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|a 9789811067815
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|a Packwood, Daniel
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| 245 |
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|a Bayesian Optimization for Materials Science
|h Elektronische Ressource
|c by Daniel Packwood
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2017, 2017
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| 300 |
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|a VIII, 42 p. 16 illus., 12 illus. in color
|b online resource
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| 505 |
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|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
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Catalysis
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| 653 |
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|a Statistics
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| 653 |
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|a Materials
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| 653 |
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|a Materials for Energy and Catalysis
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| 653 |
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|a Force and energy
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| 653 |
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|a Mathematical physics
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| 653 |
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|a Theoretical, Mathematical and Computational Physics
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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|a SpringerBriefs in the Mathematics of Materials
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| 856 |
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|u https://doi.org/10.1007/978-981-10-6781-5?nosfx=y
|x Verlag
|3 Volltext
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|a 620.1
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| 520 |
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|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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