Development of a neuroevolution machine learning potential of Pd-Cu-Ni-P alloys

被引:16
作者
Zhao, Rui [1 ]
Wang, Shucheng [1 ]
Kong, Zhuangzhuang [1 ]
Xu, Yunlei [1 ]
Fu, Kuan [1 ]
Peng, Ping [1 ]
Wu, Cuilan [1 ]
机构
[1] Hunan Univ, Sch Mat Sci & Engn, Changsha 410082, Peoples R China
基金
中国国家自然科学基金;
关键词
Pd-Cu-Ni-P alloy; Neuroevolution machine learning potential; Metallic glass; Molecular dynamics simulation; BULK-METALLIC-GLASS; MEDIUM-RANGE ORDER; MOLECULAR-DYNAMICS; PHASE-TRANSITION; EVOLUTION; MICROSTRUCTURE; DIFFUSION;
D O I
10.1016/j.matdes.2023.112012
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Pd-Cu-Ni-P alloy is an ideal model system of metallic glass known for its exceptional glass-forming ability. However, few correlation of structures with properties was systematically investigated owing to a lack of interatomic potential. In this work, a neuroevolution machine learning potential (NEP) with efficiency close to embedded atom method (EAM) potentials is developed. Its accuracy has been compared to density functional theory (DFT) calculations. For energy, force and virial, the training errors are 6.0 meV/ atom, 111.1 meV/& ANGS; and 21.5 meV/atom, respectively. By means of this NEP, several thermodynamic parameters such as glass transition temperatures and pair distribution functions of Pd40Cu30Ni10P20 and Pd40Ni40P20 liquid and glassy alloys as well as their short-range orders, tensile and compression strengths, transport properties etc. have been evaluated by a series of molecular dynamics simulations. A good agreement with DFT calculations and previous experiments indicates this NEP provides an accurate and efficient scheme in the analysis and exploration of microstructures, thermodynamic and kinetic properties of Pd-Cu-Ni-P alloys.@2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
引用
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页数:9
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