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

Gao, Jin; Wang, Yifan; Hou, Jianxin; You, Junhua; Qiu, Keqiang; Zhang, Suode; Wang, Jianqiang

doi:10.3390/met13020283

Open AccessArticle

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

by

Jin Gao

^1,2,†,

Yifan Wang

^2,†,

Jianxin Hou

³

,

Junhua You

^1,*

,

Keqiang Qiu

¹,

Suode Zhang

^2,* and

Jianqiang Wang

²

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

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Metals 2023, 13(2), 283; https://doi.org/10.3390/met13020283

Submission received: 31 December 2022 / Revised: 24 January 2023 / Accepted: 29 January 2023 / Published: 31 January 2023

(This article belongs to the Special Issue Amorphous and High-Entropy Alloy Coatings)

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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.

Keywords: machine learning; high entropy alloys; ensemble methods; phase prediction; visualized process

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MDPI and ACS Style

Gao, J.; Wang, Y.; Hou, J.; You, J.; Qiu, K.; Zhang, S.; Wang, J. Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology. Metals 2023, 13, 283. https://doi.org/10.3390/met13020283

AMA Style

Gao J, Wang Y, Hou J, You J, Qiu K, Zhang S, Wang J. Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology. Metals. 2023; 13(2):283. https://doi.org/10.3390/met13020283

Chicago/Turabian Style

Gao, Jin, Yifan Wang, Jianxin Hou, Junhua You, Keqiang Qiu, Suode Zhang, and Jianqiang Wang. 2023. "Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology" Metals 13, no. 2: 283. https://doi.org/10.3390/met13020283

APA Style

Gao, J., Wang, Y., Hou, J., You, J., Qiu, K., Zhang, S., & Wang, J. (2023). Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology. Metals, 13(2), 283. https://doi.org/10.3390/met13020283

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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