Predicting the phase behaviors of superionic water at planetary conditions

TL;DR

This paper employs machine learning and free energy calculations to predict superionic water phases under planetary conditions, providing insights into phase stability and transitions relevant to planetary interiors.

Contribution

It introduces a novel combination of machine learning and free energy methods to study superionic water, overcoming quantum simulation limitations and predicting phase behaviors at extreme conditions.

Findings

A stable close-packed superionic phase with mixed stacking is identified.

A body-centered cubic phase is kinetically favored but only thermodynamically stable in a narrow window.

Phase boundaries align with scarce experimental data, clarifying water phases in ice giants.

Abstract

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. We predict that a close-packed superionic phase with mixed stacking is stable over a wide temperature and pressure range, while a body-centered cubic 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.