eprintid: 4389
rev_number: 6
eprint_status: archive
userid: 279
dir: disk0/00/00/43/89
datestamp: 2021-03-15 04:23:59
lastmod: 2021-03-15 04:23:59
status_changed: 2021-03-15 04:23:59
type: conference_item
succeeds: 4291
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: tymmt@waseda.jp
title: Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization
ispublished: inpress
subjects: IT
subjects: Phys
divisions: fac_fit
note: Title and abstract of the paper will probably changed upon reviewers' recommendation. Latest version will be updated soon.
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  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.
date: 2021-04
date_type: published
full_text_status: none
pres_type: paper
event_title: 13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021)
event_location: Phuket, Thailand
event_dates: 7-10 Apr 2021
event_type: conference
refereed: TRUE
citation:   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)