eprintid: 4236
rev_number: 10
eprint_status: archive
userid: 279
dir: disk0/00/00/42/36
datestamp: 2020-12-11 01:36:19
lastmod: 2020-12-11 01:45:45
status_changed: 2020-12-11 01:36:19
type: conference_item
metadata_visibility: no_search
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 Machine Learning Methods
ispublished: submitted
subjects: IT
subjects: Phys
divisions: fac_fit
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 accurately predicting materials properties. Properties of multi-principal element alloys (MPEAs) highly depend on alloys’ phases. Thus accurate prediction of alloy’s phase is important to eliminate 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 alloy’s phase prediction. Comparative experiments show that on a 118 MPEAs dataset, our solution achieves cross-validation accuracy of 90%. It is 6.7% higher than that of ANN. On another 401 MPEAs dataset, our solution is comparable to ANN and obtains 70.7% cross-validation accuracy.
date: 2021
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 Machine Learning Methods.  In: 13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021), 7-10 Apr 2021, Phuket, Thailand.    (Submitted)