rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Bayesian Optimization for Materials Science
  name:
    name part: Packwood, Daniel.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VIII, 42 p. 16 illus., 12 illus. in color. online resource.
  abstract: 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.
  table of 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.
  note: by Daniel Packwood.
  subject:
    topic: Materials science
  subject:
    topic: Statistics
  subject:
    topic: Materials Science
  subject:
    topic: Energy Materials
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Statistical Physics and Dynamical Systems
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in the Mathematics of Materials, 3
  identifier: 9789811067815
  identifier: http://dx.doi.org/10.1007/978-981-10-6781-5
  record info:
    record creation date: 171004
    record change date: 20171004115402.0
    record identifier: 978-981-10-6781-5