| 期刊全称 | Bayesian Optimization for Materials Science | | 影响因子2023 | Daniel Packwood | | 视频video | | | 发行地址 | Is a timely publication as Bayesian optimization gains interest in materials science, and is one of the few introductions to this method for materials scientists.Makes the mathematical content appeali | | 学科分类 | SpringerBriefs in the Mathematics of Materials | | 图书封面 |  | | 影响因子 | 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 B | | Pindex | Book 2017 |
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发表于 2025-3-21 16:05:38
