Come in, the water is superionic

The interiors of Uranus and Neptune each contain about 50,000 times the amount of water in Earth’s oceans, and a form of water known as superionic water is believed to be stable at depths greater than about a third. from the radius of those ice giants.

Superionic water is a phase of H₂O where hydrogen atoms become liquid while oxygen atoms remain solid on a crystal lattice. Although superionic water was proposed more than three decades ago, its optical properties and oxygen networks have only recently been accurately measured in the experiments of Marius Millot and Federica Coppari of the LLNL, and many properties of this hot “black ice” are still unknown.

Scientists at Lawrence Livermore National Laboratory (LLNL) have developed a new approach using machine learning to study the phase behaviors of superionic water with unprecedented resolution.

Buried deep within the cores of the planets, much of the water in the universe can be superionic, and understanding its thermodynamic and transport properties is crucial for planetary science, but difficult to fathom experimentally or theoretically.

Under the pressures and temperatures found in giant ice planets, most of this water has been predicted by First Principle Molecular Dynamics Simulations (FPMD) to be in a superionic phase. However, such quantum mechanical simulations have traditionally been limited to short simulation times (10 s of picoseconds) and small system size (100 s of atoms) leading to significant uncertainty in the location of the limits of. phase such as the fusion line.

In superionic water experiments, sample preparation is extremely difficult, hydrogen positions cannot be determined, and temperature measurements in dynamic compression experiments are not straightforward. Often, experiments benefit from the guidance provided by quantum molecular dynamics simulations both during the design phase and for the interpretation of results.

In the most recent research, the team has taken a leap forward in their ability to deal with large system sizes and long-term timescales using machine learning techniques to learn atomic interactions from quantum mechanical calculations. They then used this machine-learned potential to drive molecular dynamics and enable the use of advanced free energy sampling methods to accurately determine 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 under extreme conditions,” a said LLNL physicist Sébastien Hamel, co-author of an article published in Physics of nature.

The team found that phase boundaries, which are consistent with existing experimental observations, help resolve insulating ice fractions, various superionic phases, and liquid water inside ice giants.

The construction of efficient interaction potentials that maintain the accuracy of quantum mechanical calculations is a difficult task. The framework that has been developed here is general and can be used to discover and / or characterize other complex materials such as battery electrolytes, plastics and nanocrystalline diamond used in ICF capsules as well as novel ammonia phases. , salts, hydrocarbons, silicates and associated mixtures. that are relevant to planetary science.

“Our quantitative understanding of superionic water sheds light on the interior structure, evolution, and magnetic fields of planets such as Uranus and Neptune, as well as the growing number of icy exoplanets,” Hamel said.

Researchers from the University of Cambridge, the University of Lyon and the University of Tohoku also contributed to the article. The LLNL portion of the research is funded by the laboratory-led research and development project “Unraveling the Physics and Chemistry of low-Z Mixtures at Extreme Pressures and Temperatures” and the Institutional Computing Grand Challenge program.