Paper Details

Published: 2021/09/23

Journal: Nature Physics

Pages: 1228--1232

DOI: 10.1038/s41567-021-01334-9


Understanding the thermodynamic and transport properties of water is crucial for planetary science. This paper uses machine learning to characterise the phase behaviours of water at extreme 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 behaviours of water at extreme conditions. 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, whereas a body-centred cubic superionic phase is only thermodynamically stable in a small window but is kinetically favoured. Our phase boundaries, which are consistent with existing—albeit scarce—experimental observations, help resolve the fractions of insulating ice, different superionic phases and liquid water inside ice giants.


Bingqing Cheng

Mandy Bethkenhagen

Chris J. Pickard

Sebastien Hamel