9:00 AM - 10:30 AM
[SIT16-P19] Thermal conductivity of hydrous Wadsleyite determined by non-equilibrium molecular dynamics based on machine learning
Keywords:Thermal conductivity, Hydrous Wadsleyite, Machine learning
In this study, we developed a high-quality machine learning potential for wadsleyite with data from first-principles calculations. Combining non-equilibrium molecular dynamics simulations and machine learning potential, we predicted the thermal conductivity of hydrous and dry wadsleyite at high temperature and pressure. We found that the thermal conductivity of wadsleyite is anisotropic and is reduced by ~10% in the pressure and temperature conditions of the MTZ by the presence of 0.81wt.% water. Heat flow to slabs may follow the direction with relatively low thermal conductivity due to the anisotropy and lattice-preferred orientation of olivine and wadsleyite, which further prevents the heating of slabs. Low temperatures increase the survival depth of hydrous and metastable minerals, improving the ability of slabs to transport water, and increasing the maximum depth of deep-focus earthquakes.