Melting of MgSiO3 determined by machine learning potentials

Abstract

The melting temperature of MgSiO${}_{3}$ is crucial in controlling the interior structures and dynamics of Earth and super-Earths. Here, the authors propose an iterative learning scheme that combines enhanced sampling, feature selection, and deep learning, and develop a unified machine learning potential of $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ quality. This is valid over a wide pressure-temperature range to determine the melting temperature of MgSiO${}_{3}$. Modeling based on these results shows that heat flux from the core to the mantle is favorable of generating magnetic fields.