<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Phase Prediction of Multi-principal Element Alloys Using Machine Learning Methods"^^ . "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."^^ . "2021" . . . . . . . . . . . . . . . "Masatoshi"^^ . "Kubo"^^ . "Masatoshi Kubo"^^ . . "Tomoyuki"^^ . "Yamamoto"^^ . "Tomoyuki Yamamoto"^^ . . "Viet Hai"^^ . "Le"^^ . "Viet Hai Le"^^ . . "Hai Chau"^^ . "Nguyen"^^ . "Hai Chau Nguyen"^^ . . . . "13th Asian Conference on Intelligent Information and Database Systems 2021 (ACIIDS 2021)"^^ . . . . . "Phuket, Thailand"^^ . . . . . "HTML Summary of #4236 \n\nPhase Prediction of Multi-principal Element Alloys Using Machine Learning Methods\n\n" . "text/html" . . . "Information Technology (IT)"@en . . . "Engineering Physics" . .