Bayesian Optimization for Materials Science / / by Daniel Packwood
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| Autore: |
Packwood Daniel
|
| Titolo: |
Bayesian Optimization for Materials Science / / by Daniel Packwood
|
| Pubblicazione: | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2017 |
| Edizione: | 1st ed. 2017. |
| Descrizione fisica: | 1 online resource (VIII, 42 p. 16 illus., 12 illus. in color.) |
| Disciplina: | 519.3 |
| Soggetto topico: | Materials |
| Catalysis | |
| Force and energy | |
| Statistics | |
| Mathematical physics | |
| Materials for Energy and Catalysis | |
| Statistical Theory and Methods | |
| Theoretical, Mathematical and Computational Physics | |
| Nota di bibliografia: | Includes bibliographical references at the end of each chapters. |
| Nota di contenuto: | 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Bayesian Optimization for Materials Science |
| ISBN: | 9789811067815 |
| 9811067813 | |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910254287803321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
SpringerBriefs in the Mathematics of Materials, . 2365-6344 ; ; 3