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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2021: Intelligent Information and Database Systems pp 155–167Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

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 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 weight 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.

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Acknowledgement

This work was partly carried out at the Joint Research Center for Environmentally Conscious Technologies in Materials Science (Project No. 02007, Grant No. JPMXP0618217637) at ZAIKEN, Waseda University.

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Authors and Affiliations

  1. Faculty of Information Technology, VNU University of Engineering and Technology, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

    Nguyen Hai Chau & Le Viet Hai

  2. Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, 169-8050, Japan

    Masatoshi Kubo & Tomoyuki Yamamoto

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  1. Nguyen Hai Chau
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  2. Masatoshi Kubo
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  3. Le Viet Hai
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  4. Tomoyuki Yamamoto
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Corresponding author

Correspondence to Nguyen Hai Chau .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Chau, N.H., Kubo, M., Hai, L.V., Yamamoto, T. (2021). Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73279-0

  • Online ISBN: 978-3-030-73280-6

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