Researchers at Lawrence Livermore National Laboratory have developed machine learning techniques to study the phase behavior of superionic water - a phase in which hydrogen becomes liquid while oxygen remains solid-like on a crystalline lattice - with unprecedented resolving power. It is thought that superionic water can exist at depths greater than approximately one third of the radii of Uranus and Neptune, as it makes up the majority of the mantles of these ice-giant planets. Over three decades ago, superionic water was first proposed; however, it started to be measured recently and its optical properties (partly opaque) and oxygen lattices have been discovered recently. Planetary science depends heavily on understanding its properties, which are difficult to determine experimentally or theoretically. As a result of shorter simulation timeframes and small system sizes, superionic water simulations typically get a bit uncertain about the location of phase boundaries such a...
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