An efficient machine learning approach to establish structure-property linkages
SCIE
SCOPUS
- Title
- An efficient machine learning approach to establish structure-property linkages
- Authors
- Jung, Jaimyun; Yoon, Jae Ik; Park, Hyung Keun; Kim, Jin You; Kim, Hyoung Seop
- Date Issued
- 2019-01
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/95334
- DOI
- 10.1016/j.commatsci.2018.09.034
- ISSN
- 0927-0256
- Article Type
- Article
- Citation
- COMPUTATIONAL MATERIALS SCIENCE, vol. 156, page. 17 - 25, 2019-01
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