Publication
Machine Learning-Based Experimental Design in Materials Science
MDR Open Deposited
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.
- First published at
- Creator
- Keyword
- Resource type
- Publisher
- Date published
- 16/01/2018
- Rights statement
- Licensed Date
- 16/01/2018
- Journal
- Language
- Last modified
- 16/10/2020
Items
Thumbnail | Title | Date Uploaded | Size | Visibility | Actions |
---|---|---|---|---|---|
|
Dieb-Tsuda2018_Chapter_MachineLearning-BasedExperimen.pdf | 02/10/2020 | 454 KB | MDR Open |
|