Nguyen, Hai Chau and Kubo, Masatoshi and Le, Viet Hai and Yamamoto, Tomoyuki (2021) Phase Prediction of Multi-principal Element Alloys Using Machine Learning Methods. In: 13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021), 7-10 Apr 2021, Phuket, Thailand. (Submitted)
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Abstract
Designing new materials with desired properties is a complex and time consuming process. One of the challenging factors of the design process is the huge search space of possible materials. Machine learning methods such as k-nearest neighbours, support vector machine (SVM) and artificial neural network (ANN) can contribute to this process by accurately predicting materials properties. Properties of multi-principal element alloys (MPEAs) highly depend on alloys’ phases. Thus accurate prediction of alloy’s phase is important to eliminate the search space. In this paper we propose a solution of employing support vector machine method with hyperparameters tuning and the use of weights values for alloy’s phase prediction. Comparative experiments show that on a 118 MPEAs dataset, our solution achieves cross-validation accuracy of 90%. It is 6.7% higher than that of ANN. On another 401 MPEAs dataset, our solution is comparable to ANN and obtains 70.7% cross-validation accuracy.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Information Technology (IT) Engineering Physics |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Hải Châu Nguyễn |
| Date Deposited: | 11 Dec 2020 01:36 |
| Last Modified: | 11 Dec 2020 01:45 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4236 |
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- Phase Prediction of Multi-principal Element Alloys Using Machine Learning Methods. (deposited 11 Dec 2020 01:36) [Currently Displayed]
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