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.

宇宙中的大多数水可能是超离子的，其热力学和输运特性对行星科学至关重要，但难以通过实验或理论进行探索。我们使用机器学习和自由能方法来克服量子力学模拟的局限性，并描述极端条件下水的氢扩散、超离子跃迁和相行为。我们预测，对于有限系统而言，具有一小部分混合堆叠的密排超离子相在很宽的温度和压力范围内是稳定的，而体心立方超离子相仅在小窗口内热力学稳定，但在动力学上更受青睐. 我们的相界与现有的（尽管很少）实验观察结果一致，有助于解决绝缘冰的碎片，