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In materials design and discovery processes, optimal experimental design (OED) algorithms are getting more popular. OED is often modeled as an optimization of a black-box function. In this chapter, we introduce two machine learning-based approaches for OED: Bayesian optimization (BO) and Monte Carlo tree search (MCTS). BO is based on a relatively complex machine learning model and has been proven effective in a number of materials design problems. MCTS is a simpler and more efficient approach that showed significant success in the computer Go game. We discuss existing OED applications in materials science and discuss future directions.

権利情報:

• Creative Commons BY Attribution 4.0 International
  Creative Commons BY Attribution 4.0 International

キーワード: Materials design, Machine learning, Optimal experiment design

刊行年月日: 2018-01-16

出版者: Springer, Singapore

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研究助成金:

原稿種別: 論文以外のデータ

MDR DOI:

公開URL: https://doi.org/10.1007/978-981-10-7617-6_4

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更新時刻: 2022-10-03 01:58:06 +0900

MDRでの公開時刻: 2021-08-14 03:55:36 +0900