Thermal Conductivity of MgSiO3‐H2O System Determined by Machine Learning Potentials

Abstract

Abstract Thermal conductivity plays a pivotal role in understanding the dynamics and evolution of Earth's interior. The Earth's lower mantle is dominated by MgSiO3 polymorphs which may incorporate trace amounts of water. However, the thermal conductivity of MgSiO3‐H2O binary system remains poorly understood. Here, we calculate the thermal conductivity of water‐free and water‐bearing bridgmanite, post‐perovskite, and MgSiO3 melt, using a combination of Green‐Kubo method with molecular dynamics simulations based on a machine learning potential of ab initio quality. The thermal conductivities of water‐free bridgmanite and post‐perovskite overall agree well with previous theoretical and experimental studies. The presence of water mildly reduces the thermal conductivity of the host minerals, significantly weakens the temperature dependence of the thermal conductivity, and reduces the thermal anisotropy of post‐perovskite. Overall, water reduces the thermal conductivity difference between bridgmanite and post‐perovskite, and thus may attenuate lateral heterogeneities of the core‐mantle boundary heat flux.

Keywords

• thermal conductivity
• bridgmanite
• post‐perovskite
• lower mantle
• machine learning