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DOI | 10.1029/2021GL093806 |
Thermal conductivity of silicate liquid determined by machine learning potentials | |
Jie Deng; Lars Stixrude | |
2021-08-19 | |
发表期刊 | Geophysical Research Letters |
出版年 | 2021 |
英文摘要 | Silicate liquids are important agents of thermal evolution, yet their thermal conductivity is largely unknown. Here, we determine the thermal conductivity of a silicate liquid by combining the Green-Kubo method with a machine learning potential of ab initio quality over the entire pressure regime of the mantle. We find that the thermal conductivity of MgSiO3 liquid is 1.1 W m-1 K-1 at the 1 bar melting point, and 4.0 W m-1 K-1 at core-mantle boundary conditions. The thermal conductivity increases with compression while remaining nearly constant on isochoric heating. The pressure dependence arises from the increasing bulk modulus on compression, and the weak temperature dependence arises from the saturation of the phonon mean free path due to structural disorder. The thermal conductivity of silicate liquids is less than that of ambient mantle, a contrast that may be important for understanding melt generation, and heat flux from the core. |
领域 | 气候变化 |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/335927 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Jie Deng,Lars Stixrude. Thermal conductivity of silicate liquid determined by machine learning potentials[J]. Geophysical Research Letters,2021. |
APA | Jie Deng,&Lars Stixrude.(2021).Thermal conductivity of silicate liquid determined by machine learning potentials.Geophysical Research Letters. |
MLA | Jie Deng,et al."Thermal conductivity of silicate liquid determined by machine learning potentials".Geophysical Research Letters (2021). |
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