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 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.
| Original language | English |
|---|---|
| Pages (from-to) | 1228-1232 |
| Number of pages | 5 |
| Journal | Nature Physics |
| Volume | 17 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 1 Nov 2021 |
| Externally published | Yes |
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