Research from the Laboratory for Laser Energetics paves the way for more accurate computer models, which are needed to understand the interior of planets and the physical properties of nuclear fusion.
Hydrogen is one of the most abundant elements in the universe.
On Earth, hydrogen is normally a gas. But when it is under high temperatures and pressures糖心传媒攖he conditions that exist within many planets, such as Jupiter糖心传媒攈ydrogen goes through a series of phase transitions and takes on the properties of a liquid metal. One of the metallic properties it takes on is becoming an electrical conductor.
In in Nature糖心传媒檚 糖心传媒淢atters Arising,糖心传媒� researchers at the (LLE), including lead author , an LLE staff scientist; graduate student Josh Hinz; and , an associate professor of and a distinguished scientist at the LLE, respond to a that used machine learning techniques to study the liquid-liquid phase transitions of dense hydrogen from an insulating liquid to a liquid metal.
In their response, Karasiev and his colleagues outline how these machine learning techniques produced incorrect results in describing hydrogen糖心传媒檚 phase transitions. Their research has important implications in building more accurate computer models to study hydrogen, which can lead to a better understanding of the interiors of planets and stars and the physical properties of processes like nuclear fusion.
When building the equation-of-state of hydrogen糖心传媒攖he equation that describes the state of hydrogen under various physical conditions糖心传媒攊t is important to characterize the transition into the metallic hydrogen phase: Is it an abrupt (sharp) transition or a smooth transition?
糖心传媒淭his physics character of first-order phase transition can have profound implications in understanding what giant planets糖心传媒� interior structures look like, such as de-mixing of hydrogen and helium in Jupiter,糖心传媒� Hu says.
In the 2020 Nature paper, researchers used machine learning and concluded the transition of hydrogen to the metallic hydrogen phase was smooth. Karasiev and his colleagues, however, performed large-scale quantum simulations using other fundamental density-functional theory and found that hydrogen糖心传媒檚 transition is not smooth, but is instead more abrupt. This is consistent with other previous data collected without machine learning.
糖心传媒淥ur work demonstrated that machine learning can fool scientists if they are not careful when using machine learning to study phase-transition boundaries,糖心传媒� Karasiev says. 糖心传媒淭his is an important step in building better models to outline how hydrogen can become metallic hydrogen.糖心传媒�
The National Science Foundation and the US Department of Energy National Nuclear Security Administration funded this research.
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