TY - GEN
T1 - Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization
AU - Chau, Nguyen Hai
AU - Kubo, Masatoshi
AU - Hai, Le Viet
AU - Yamamoto, Tomoyuki
N1 - Funding Information:
Acknowledgement. This work was partly carried out at the Joint Research Center for Environmentally Conscious Technologies in Materials Science (Project No. 02007, Grant No. JPMXP0618217637) at ZAIKEN, Waseda University.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
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 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 weight 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.
AB - 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 weight 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.
KW - Bayesian optimization
KW - High-entropy alloys
KW - Multi-principal element alloys
KW - Phase prediction
KW - Support vector machine
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U2 - 10.1007/978-3-030-73280-6_13
DO - 10.1007/978-3-030-73280-6_13
M3 - Conference contribution
AN - SCOPUS:85104690128
SN - 9783030732790
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 155
EP - 167
BT - Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings
A2 - Nguyen, Ngoc Thanh
A2 - Chittayasothorn, Suphamit
A2 - Niyato, Dusit
A2 - Trawiński, Bogdan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021
Y2 - 7 April 2021 through 10 April 2021
ER -