{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:32:35Z","timestamp":1762918355471},"reference-count":41,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"ZAIKEN, Waseda University","award":["02114","JPMXP0621467974"],"award-info":[{"award-number":["02114","JPMXP0621467974"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vietnam J. Comp. Sci."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> 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 [Formula: see text]-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\u2019 phase. Thus, accurate prediction of the alloy\u2019s 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\u2019s 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. <\/jats:p>","DOI":"10.1142\/s2196888822500312","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T08:16:55Z","timestamp":1655367415000},"page":"101-116","source":"Crossref","is-referenced-by-count":5,"title":["Support Vector Machine-Based Phase Prediction of Multi-Principal Element Alloys"],"prefix":"10.1142","volume":"10","author":[{"given":"Nguyen Hai","family":"Chau","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, VNU University of Engineering and Technology, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam"}]},{"given":"Masatoshi","family":"Kubo","sequence":"additional","affiliation":[{"name":"Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo 169-8050, Japan"}]},{"given":"Le 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