It is rare on the planet, but it is abundant in Jupiter. More accurate data on hydrogen's phase transition to metallic hydrogen is provided by new research at the Laboratory for Laser Energetics. Kevin M. Gill is credited with NASA / JPL-Caltech.
Hydrogen is abundant in the universe.
Hydrogen is a gas on Earth. hydrogen goes through a series of phase transitions when it is under high temperatures and pressures. One of the metallic properties it takes on is being an electrical conductor.
The University of Rochester Laboratory for Laser Energetics has a team that includes lead author Valentin Karasiev, a staff scientist, and graduate student Josh Hinz.
Karasiev and his colleagues explain how machine learning techniques produced incorrect results in describing hydrogen's phase transitions. Their research can lead to better understanding of the interiors of planets and stars, as well as the physical properties of processes like nuclear fusion, by building more accurate computer models.
When building the equation-of-state of hydrogen, it's important to understand the transition into the metallic hydrogen phase: Is it an abrupt (sharp) transition or a smooth transition?
The physics character of first-order phase transition can have profound implications in understanding what giant planets' interior structures look like.
In the 2020 Nature paper, researchers used machine learning and found the transition of hydrogen to metallic hydrogen was smooth. Karasiev and his colleagues used other fundamental density-functional theory to show that hydrogen's transition is more abrupt. This is consistent with previous data.
Karasiev says that their work shows that machine learning can fool scientists if they are not careful. This is an important step in building better models to outline how hydrogen can become metallic hydrogen.
The liquid–liquid phase transition of dense hydrogen was written by Valentin V. Karasiev. There is a DOI titled " 10.1038/s41586-021-04078-x".
Nature journal information.
Scientists reveal the limits of machine learning for hydrogen models.
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