%0 Publication %T Machine Learning-Based Experimental Design in Materials Science %A Tsuda, Koji; Dieb, Thaer M. %8 02/10/2020 %I Springer, Singapore %U https://mdr.nims.go.jp/concern/publications/1r66j202q %( https://doi.org/10.1007/978-981-10-7617-6_4 %X 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. %G English %[ 16/10/2020 %9 Part of Book %K Machine learning; Materials design; Optimal experiment design %~ MDR: NIMS Materials Data Repository %W National Institute for Materials Science