Melting of MgSiO3 determined by machine learning potentials

Melting in the deep rocky portions of planets is important for understanding the thermal evolution of these bodies and the possible generation of magnetic fields in their underlying metallic cores. But the melting tempera-ture of silicates is poorly constrained at the pressures expected in super-Earth exoplanets, the most abundant type of planets in the galaxy. Here, we propose an iterative learning scheme that combines enhanced sampling, feature selection, and deep learning, and develop a unified machine learning potential of ab initio quality valid over a wide pressure-temperature range to determine the melting temperature of MgSiO3. The melting temperature of the high-pressure, post-perovskite phase, important for super-Earths, increases more rapidly with increasing pressure than that of the lower pressure perovskite phase, stable at the base of Earth's mantle. The volume of the liquid closely approaches that of the solid phases at the highest pressure of our study. Our computed triple point constrains the Clapeyron slope of the perovskite to post-perovskite transition, which we compare with observations of seismic reflectivity at the base of Earth's mantle to calibrate Earth's core heat flux.