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Support Vector Machine-based Phase Prediction of Multi-principal Element Alloys

Nguyen, Hai Chau and Kubo, Masatoshi and Le, Viet Hai and Yamamoto, Tomoyuki (2022) Support Vector Machine-based Phase Prediction of Multi-principal Element Alloys. Vietnam Journal of Computer Science . ISSN 2196-8888 (In Press)

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Designing new materials with desired properties is a complex and time-consuming process. One of the most 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 weighted values for prediction of the alloy’s phase. Using the dataset consisting of the experimental results of 118 MPEAs, our solution achieves a 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. We also found that additional variables, including average melting temperature and standard deviation of melting temperature, increase prediction accuracy by 3.34% in the best case.

Item Type: Article
Subjects: Information Technology (IT)
Engineering Physics
Scopus-indexed journals
Divisions: Faculty of Information Technology (FIT)
Depositing User: Hải Châu Nguyễn
Date Deposited: 19 Aug 2022 05:34
Last Modified: 19 Aug 2022 05:35

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