Phase formation prediction of high-entropy alloys: a deep learning study
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.