relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4236/
title: Phase Prediction of Multi-principal Element Alloys Using Machine Learning Methods
creator: Nguyen, Hai Chau
creator: Kubo, Masatoshi
creator: Le, Viet Hai
creator: Yamamoto, Tomoyuki
subject: Information Technology (IT)
subject: Engineering Physics
description: 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
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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)