@inproceedings{SisLab4236,
       booktitle = {13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021)},
           title = {Phase Prediction of Multi-principal Element Alloys Using Machine Learning Methods},
          author = {Hai Chau Nguyen and Masatoshi Kubo and Viet Hai Le and Tomoyuki Yamamoto},
            year = {2021},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4236/},
        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.}
}