Journal
COMPUTATIONAL MATERIALS SCIENCE
Volume 156, Issue -, Pages 17-25Publisher
ELSEVIER
DOI: 10.1016/j.commatsci.2018.09.034
Keywords
Microstructure; Machine learning; Gaussian process regression; Optimization
Categories
Funding
- POSCO [2017Y054]
- Brain Korea 21 PLUS project for Center for Creative Industrial Materials [F16SN25D1706]
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Full-field simulations with synthetic microstructure offer unique opportunities in predicting and understanding the linkage between microstructural variables and properties of a material prior to or in conjunction with experimental efforts. Nevertheless, the computational cost restrains the application of full-field simulations in optimizing materials microstructures or in establishing comprehensive structure-property linkages. To address this issue, we propose the use of machine learning technique, namely Gaussian process regression, with a small number of full-field simulation results to construct structure-property linkages that are accurate over a wide range of microstructures. Furthermore, we demonstrate that with the implementation of expected improvement algorithm, microstructures that exhibit most desirable properties can be identified using even smaller number of full-field simulations.
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