Water is the dominant constituent of Uranus’ and Neptune’s mantles, and superionic water is believed to be stable at depths greater than about one-third of the radius of these ice-giant planets.
Although superionic water was postulated over three decades ago, its optical properties (it is partially opaque) and oxygen lattices were only accurately measured recently, and many properties are still uncharted.
Understanding its properties is crucial for planetary science but difficult to probe experimentally or theoretically.
Quantum-mechanical simulations of superionic water have traditionally been limited to short simulation times and small system size, leading to significant uncertainty in the location of phase boundaries such as the melting line.
In the new research, Dr. Sebastien Hamel of Lawrence Livermore National Laboratory and colleagues made a leap forward in its ability to treat large system sizes and long-time scales by making use of machine learning techniques to learn the atomic interactions from quantum mechanical calculations.
The researchers then used that machine-learned potential to drive the molecular dynamics and enable the use of advanced free energy sampling methods to accurately determine the phase boundaries.
“We use machine learning and free energy methods to overcome the limitations of quantum mechanical simulations, and characterize hydrogen diffusion, superionic transitions and phase behaviors of water at extreme conditions,” Dr. Hamel said.
To read more, click here.