Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 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.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings
EditorsNgoc Thanh Nguyen, Suphamit Chittayasothorn, Dusit Niyato, Bogdan Trawiński
PublisherSpringer Science and Business Media Deutschland GmbH
Pages155-167
Number of pages13
ISBN (Print)9783030732790
DOIs
Publication statusPublished - 2021
Event13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 - Phuket, Thailand
Duration: 2021 Apr 72021 Apr 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12672 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021
CountryThailand
CityPhuket
Period21/4/721/4/10

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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