Support Vector Machine-Based Phase Prediction of Multi-Principal Element Alloys

Nguyen Hai Chau*, Masatoshi Kubo, Le Viet Hai, Tomoyuki Yamamoto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Designing new materials with desired properties is a complex and time-consuming process. One of the most challenging factors of the design process is the huge search space of possible materials. Machine learning methods such as k-nearest neighbors, 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 SVM method with hyperparameters tuning and the use of weighted values for prediction of the alloy's phase. Using the dataset consisting of the experimental results of 118 MPEAs, our solution achieves a 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. We also found that additional variables, including average melting temperature and standard deviation of melting temperature, increase prediction accuracy by 3.34% in the best case.

Original languageEnglish
JournalVietnam Journal of Computer Science
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Bayesian optimization
  • High-entropy alloys
  • Multi-principal element alloys
  • Phase prediction
  • Support vector machine

ASJC Scopus subject areas

  • Software
  • Computer Science (miscellaneous)
  • Information Systems
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence

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