TY  - CONF
ID  - SisLab4236
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4236/
A1  - Nguyen, Hai Chau
A1  - Kubo, Masatoshi
A1  - Le, Viet Hai
A1  - Yamamoto, Tomoyuki
Y1  - 2021///
N2  - 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.
TI  - Phase Prediction of Multi-principal Element Alloys Using Machine Learning Methods
M2  - Phuket, Thailand
AV  - none
T2  - 13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021)
ER  -