GSTDTAP  > 气候变化
DOI10.1029/2021GL093806
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
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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
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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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