Thermal Conductivity of Silicate Liquid Determined by Machine Learning Potentials

Jie Deng, Lars Stixrude

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere2021GL093806
JournalGeophysical Research Letters
Volume48
Issue number17
DOIs
StatePublished - Sep 16 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Geophysics
  • General Earth and Planetary Sciences

Keywords

  • ab initio
  • machine learning
  • silicate liquid
  • thermal conductivity

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