@inproceedings{SisLab4389,
       booktitle = {13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021)},
           month = {April},
           title = {Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization},
          author = {Hai Chau Nguyen and Masatoshi Kubo and Viet Hai Le and Tomoyuki Yamamoto},
            year = {2021},
            note = {Title and abstract of the paper will probably changed upon reviewers' recommendation. Latest version will be updated soon.},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4389/},
        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 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  weights 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.}
}