Developing a machine learning Ni-Co-Al ternary interatomic potential for temperature and strain-rate dependent mechanical behaviors of Ni-based single crystal superalloys

Ni-based single crystal (SX) superalloys, with excellent mechanical properties in high temperature, are widely used as the preferred materials in the aerospace field. Atomistic simulations provide an effective means to understand the underlying physics of microstructure-property relationship. However, the reliability of atomistic simulation results highly depends on the precision of potential. In this work, a machine learning potential (MLP) for the ternary Ni-Co-Al system is developed using neuroevolution potential (NEP) framework which maintains a high degree of fidelity and computational efficiency from ab initio molecular dynamics simulations (AIMD). It is demonstrated that the Ni-Co-Al MLP can be used to investigate the temperature and strain-rate dependent mechanical behaviors of Ni-based superalloys with the effect of precipitation. Specifically, the following case studies about γ / γ ′ system are conducted: (1) the temperature and strain rate effect on the γ / γ ′ phase boundary characteristics and uniaxial tensile deformation behaviors; (2) the shock-induced phase transformation and spallation behaviors at different strain rates. The atomistic findings promote the fundamental understanding of the mechanical responses of Ni-based SX superalloys with precipitation at different temperature and strain rate conditions. Moreover, the MLP development process proposed in this work provides a feasible framework to develop diverse multicomponent interatomic potentials. • A highly precise, reliable and efficient Ni-Co-Al machine learning potential (MLP) is developed using NEP framework. • The developed Ni-Co-Al MLP is greatly suitable for the study of Ni-based superalloys under different temperatures and strain rates. • The smaller the strain rate, the higher the temperature at which anomalous yield enhancement occurs. • The coupling effect of crystal orientation and shock velocity on phase transformation and spallation modes is revealed.

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Publication Details

Journal
Materials & Design
Published
2025-08-12
DOI
https://doi.org/10.1016/j.matdes.2025.114535
Citations
2
Primary Topic
High Temperature Alloys and Creep
Type
article
Field-Weighted Citation Impact
1.43

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article

Developing a machine learning Ni-Co-Al ternary interatomic potential for temperature and strain-rate dependent mechanical behaviors of Ni-based single crystal superalloys

Ligang Sun, Linli Zhu, Penghua Ying, Dongfeng Li et al.
2 citations
Materials & Design
High Temperature Alloys and Creep
1.43
article

Developing a machine learning Ni-Co-Al ternary interatomic potential for temperature and strain-rate dependent mechanical behaviors of Ni-based single crystal superalloys

Ligang Sun, Linli Zhu, Penghua Ying, Dongfeng Li, Zhijia Qin, Shirong Liang
article en
2 citations

Abstract

Ni-based single crystal (SX) superalloys, with excellent mechanical properties in high temperature, are widely used as the preferred materials in the aerospace field. Atomistic simulations provide an effective means to understand the underlying physics of microstructure-property relationship. However, the reliability of atomistic simulation results highly depends on the precision of potential. In this work, a machine learning potential (MLP) for the ternary Ni-Co-Al system is developed using neuroevolution potential (NEP) framework which maintains a high degree of fidelity and computational efficiency from ab initio molecular dynamics simulations (AIMD). It is demonstrated that the Ni-Co-Al MLP can be used to investigate the temperature and strain-rate dependent mechanical behaviors of Ni-based superalloys with the effect of precipitation. Specifically, the following case studies about γ / γ ′ system are conducted: (1) the temperature and strain rate effect on the γ / γ ′ phase boundary characteristics and uniaxial tensile deformation behaviors; (2) the shock-induced phase transformation and spallation behaviors at different strain rates. The atomistic findings promote the fundamental understanding of the mechanical responses of Ni-based SX superalloys with precipitation at different temperature and strain rate conditions. Moreover, the MLP development process proposed in this work provides a feasible framework to develop diverse multicomponent interatomic potentials. • A highly precise, reliable and efficient Ni-Co-Al machine learning potential (MLP) is developed using NEP framework. • The developed Ni-Co-Al MLP is greatly suitable for the study of Ni-based superalloys under different temperatures and strain rates. • The smaller the strain rate, the higher the temperature at which anomalous yield enhancement occurs. • The coupling effect of crystal orientation and shock velocity on phase transformation and spallation modes is revealed.

Materials & DesignVol. 258
Tel Aviv University (IL), Harbin Institute of Technology (CN), Zhejiang University (CN)
Science, Technology and Innovation Commission of Shenzhen Municipality, Development and Reform Commission of Shenzhen Municipality, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 18%
High Temperature Alloys and Creep
1.43
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