化学物理
物理
相(物质)
预熔
能源景观
扩散
航程(航空)
相图
纳米孔
堆积
凝聚态物理
相变
热力学
纳米技术
材料科学
量子力学
熔点
核磁共振
复合材料
作者
Bingqing Cheng,Mandy Bethkenhagen,Chris J. Pickard,Sébastien Hamel
出处
期刊:Nature Physics
[Nature Portfolio]
日期:2021-09-23
卷期号:17 (11): 1228-1232
被引量:51
标识
DOI:10.1038/s41567-021-01334-9
摘要
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. Superionic water is believed to exist in the interior of ice giant planets. By combining machine learning and free-energy methods, the phase behaviours of water at the extreme pressures and temperatures prevalent in such planets are predicted.
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