Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology

Author:

Gao Jin12,Wang Yifan2,Hou Jianxin3,You Junhua1ORCID,Qiu Keqiang1,Zhang Suode2,Wang Jianqiang2

Affiliation:

1. School of Materials Science and Engineering, Shenyang University of Technology, Shenyang 110870, China

2. Shenyang National Laboratory for Materials Science, Institute of Metal Research, CAS, Shenyang 110016, China

3. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China

Abstract

High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to predict the phase of high entropy alloys in a database consisting of 511 labeled data. Before the models are trained, features based on the empirical design principles are selected through XGBoost, taking into account the relative importance of each feature. The ensemble learning methods of Voting and Stacking stand out among these algorithms, with a predictive accuracy of over 92%. In addition, the alloy designing process is visualized by a decision tree, introducing a new criterion for identifying phases of FCC, BCC, and FCC + BCC in high entropy alloys. These findings provide valuable information for selecting important features and suitable machine learning models in the design of high entropy alloys.

Funder

National Natural Science Foundation of China

Key Research Program of the Chinese Academy of Sciences

Key Research & Development Plan of Jiangxi Province

Basic scientific research project of Liaoning Province Department of Education

LiaoNing Revitalization Talents Program

Publisher

MDPI AG

Subject

General Materials Science,Metals and Alloys

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