eprintid: 4742
rev_number: 9
eprint_status: archive
userid: 279
dir: disk0/00/00/47/42
datestamp: 2022-08-19 05:34:26
lastmod: 2022-08-19 05:35:07
status_changed: 2022-08-19 05:35:07
type: article
metadata_visibility: show
creators_name: Nguyen, Hai Chau
creators_name: Kubo, Masatoshi
creators_name: Le, Viet Hai
creators_name: Yamamoto, Tomoyuki
creators_id: chaunh@vnu.edu.vn
creators_id: masa104k@ruri.waseda.jp
creators_id: 16020936@vnu.edu.vn
creators_id: tymmt@waseda.jp
title: Support Vector Machine-based Phase Prediction of Multi-principal Element Alloys
ispublished: inpress
subjects: IT
subjects: Phys
subjects: Scopus
divisions: fac_fit
abstract: 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.
date: 2022
date_type: completed
publisher: World Scientific
official_url: https://www.worldscientific.com/worldscinet/vjcs
contact_email: chaunh@vnu.edu.vn
full_text_status: none
publication: Vietnam Journal of Computer Science
refereed: TRUE
issn: 2196-8888
funders: Waseda University
projects: Project No. 02114, Grant No. JPMXP0621467974, the Joint Research Center for Environmen- tally Conscious Technologies in Materials Science, ZAIKEN, Waseda University.
citation:   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)