Bayesian Optimization for Materials Science. 1st ed. 2017

種類:

電子ブック

責任表示:

by Daniel Packwood

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2017

著者名:

• Packwood, Daniel.
• SpringerLink (Online service)

シリーズ名:

SpringerBriefs in the Mathematics of Materials ; 3

ISBN:

9789811067815 [9811067813]    

注記:

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 re

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