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Type: Journal article
Title: Phase formation prediction of high-entropy alloys: a deep learning study
Author: Zhu, W.
Huo, W.
Wang, S.
Wang, X.
Ren, K.
Tan, S.
Fang, F.
Xie, Z.
Jiang, J.
Citation: Journal of Materials Research and Technology, 2022; 18:800-809
Publisher: Elsevier BV
Issue Date: 2022
ISSN: 2238-7854
Statement of
Wenhan Zhu, Wenyi Huo, Shiqi Wang, Xu Wang, Kai Ren, Shuyong Tan, Feng Fang, Zonghan Xie, Jianqing Jiang
Abstract: High-entropy alloys (HEAs) represent prospective applications considering their outstanding mechanical properties. The properties in HEAs can be affected by the phase structure. Artificial neural network (ANN) is a promising machine learning approach for predicting the phases of HEAs. In this work, a deep neural network (DNN) structure using a residual network (RESNET) is proposed for the phase formation prediction of HEAs. It shows a high overall accuracy of 81.9%. Compared it with machine learning models, e.g., ANN and conventional DNN, its Micro-F1 score highlights the advantages of phase prediction of HEAs. It can remarkably prevent network degradation and improve the algorithm accuracy. It delivers a new path to develop the phase formation prediction model using deep learning, which can be of universal relevance in assisting the design of the HEAs with novel chemical compositions.
Keywords: High-entropy alloys
Phase Phase formation
Deep neural network
Residual network
Rights: © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://
DOI: 10.1016/j.jmrt.2022.01.172
Grant ID: ARC
Appears in Collections:Mechanical Engineering publications

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