Machine Learning-Based Experimental Design in Materials Science [chapter]

Thaer M. Dieb, Koji Tsuda
<span title="">2018</span> <i title="Springer Singapore"> Nanoinformatics </i> &nbsp;
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 learningbased 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
more &raquo; ... t showed significant success in the computer Go game. We discuss existing OED applications in materials science and discuss future directions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1007/978-981-10-7617-6_4</a> <a target="_blank" rel="external noopener" href="">fatcat:25mcb5ngc5bs5l5wv3m3u6hlye</a> </span>
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