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Bayesian Optimization for Materials Science (Paperback, 1st ed. 2017)
Loot Price: R1,806
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Bayesian Optimization for Materials Science (Paperback, 1st ed. 2017)
Series: SpringerBriefs in the Mathematics of Materials, 3
Expected to ship within 10 - 15 working days
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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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