Phase behavior of super-ionic water at planetary conditions.

Most water in the universe may be superionic, and its thermodynamic and transport properties are crucial for planetary science but difficult to probe experimentally or theoretically. We use machine learning and free energy methods to overcome the limitations of quantum mechanical simulations, and characterize hydrogen diffusion, superionic transitions, and phase behaviors of water at extreme conditions. The machine learned potentials allow us to explore in detail the nature of the phase transitions and the free energy surface of water and clarifies the phase behaviour of the high-pressure insulating and superionic ices and reveals peculiar solid–solid transition mechanisms not known in other systems.

We predict that close-packed superionic phases, which have a fraction of mixed stacking for finite systems, are stable over a wide temperature and pressure range, while a body-centered cubic superionic phase is only thermodynamically stable in a small window but is kinetically favored. Our phase boundaries, which are consistent with the existing-albeit scarce-experimental observations, help resolve the fractions of insulating ice, different superionic phases, and liquid water inside of ice giants.