Accelerated design of low-activation high entropy alloys with desired phase and property by machine learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Xiaochen Li
  • Mingjie Zheng
  • Chang Li
  • Wenyi Ding
  • Jie Yu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number102000
Journal / PublicationApplied Materials Today
Volume36
Online published9 Dec 2023
Publication statusPublished - Feb 2024

Abstract

Low-activation high-entropy alloys (HEAs) have been regarded as novel candidate structural materials for fusion reactors due to their excellent mechanical and radiation resistant properties. Nevertheless, the potential vast composition space brings a prominent challenge in the design of low-activation HEAs. Herein, a new strategy based on machine learning (ML) was proposed to accelerate the exploitation of low-activation HEAs with desired phase and property. Two optimized classification models with accuracy of > 85% were developed to identify single-phase body-centered-cubic (BCC) solid-solution (SS) HEAs. One regression model with correlation coefficient (R) of > 0.9 was constructed to predict the hardness of HEAs. The phase and hardness prediction models were combined in accordance with an integrated design strategy to identify the low-activation HEAs with desired phase and property from 284,634 candidates. With this design strategy, a new desired single-phase BCC Fe35Cr30V20Mn10Ti5 low-activation HEA with 555.9 ± 15.3 HV was designed and fabricated via only two experimental iterations. Besides, two new phase selection rules with accuracy of > 95% were given to efficiently screen SS and BCC HEAs, respectively. Our research work not only provides new ideas to search for low-activation HEAs, but also can be extended to the integrated design of structure and performance of other advanced materials. © 2023 Elsevier Ltd.

Research Area(s)

  • Feature screening, Hardness prediction, High entropy alloys, Machine learning, Phase classification

Citation Format(s)

Accelerated design of low-activation high entropy alloys with desired phase and property by machine learning. / Li, Xiaochen; Zheng, Mingjie; Li, Chang et al.
In: Applied Materials Today, Vol. 36, 102000, 02.2024.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review