Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics
| Autor: | Haifeng Liu, Wei-Dong Chu, Gongmu Zhang, Shuaichuang Wang, Xingyu Gao, Qiong Li, Haifeng Song, Han Wang, Jun Fang, Yuqing Cheng, Hong-Zhou Song |
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| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Range (particle radiation) Materials science General Physics and Astronomy chemistry.chemical_element Thermodynamics Potential method 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences lcsh:QC1-999 Viscosity Molecular dynamics chemistry Aluminium High pressure 0103 physical sciences Melting point Current (fluid) 0210 nano-technology lcsh:Physics |
| Zdroj: | AIP Advances, Vol 11, Iss 1, Pp 015043-015043-6 (2021) |
| ISSN: | 2158-3226 |
| DOI: | 10.1063/5.0036298 |
| Popis: | The shear viscosity of matter and efficient simulating methods in a wide range of temperatures and densities are desirable. In this study, we present the deep-learning many-body potential (the deep potential) method to reduce the computational cost of simulations for the viscosity of liquid aluminum at high temperature and high pressure with accurate results. Viscosities for densities of 2.35 g/cm3, 2.7 g/cm3, 3.5 g/cm3, and 4.27 g/cm3 and temperatures from melting points to about 50 000 K are calculated. The results agree well with the experiment data at a pressure near 1 bar and are consistent with the simulation of first-principles at high pressure and high temperature. We reveal the behavior of the shear viscosity of liquid Al at a range where the current experimental results do not exist. Based on the available experimental data and newly generated simulation data, we propose a modified Enskog–Dymond theory, which can analytically calculate the viscosity of Al at this range. This research is helpful for numerous potential applications. |
| Databáze: | OpenAIRE |
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