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Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization

Nguyen, Hai Chau and Kubo, Masatoshi and Le, Viet Hai and Yamamoto, Tomoyuki (2021) Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization. In: 13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021), 7-10 Apr 2021, Phuket, Thailand. (In Press)

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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 predicting materials properties accurately. Properties of multi-principal element alloys (MPEAs) highly depend on alloys’ phase. Thus, accurate prediction of the alloy’s phase is important to narrow down 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 prediction of the alloy’s phase. Using the dataset consisting of the experimental results of 118 MPEAs, our solution achieves the cross-validation accuracy of 90.2%. We confirm the superiority of this score over the performance of ANN statistically. On the other dataset containing 401 MPEAs, our SVM model is comparable to ANN and exhibits 70.6% cross-validation accuracy.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Title and abstract of the paper will probably changed upon reviewers' recommendation. Latest version will be updated soon.
Subjects: Information Technology (IT)
Engineering Physics
Divisions: Faculty of Information Technology (FIT)
Depositing User: Hải Châu Nguyễn
Date Deposited: 15 Mar 2021 04:23
Last Modified: 15 Mar 2021 04:23

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