Scientists reveal the limits of machine learning for hydrogen models

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--the conditions that exist within many planets, such as Jupiter--hydrogen 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 a new paper in the Nature journal's "Matters Arising," researchers at the University of Rochester Laboratory for Laser Energetics (LLE), including lead author Valentin Karasiev, an LLE staff scientist; graduate student Josh Hinz; and Suxing Hu, an associate professor of mechanical engineering and a distinguished scientist at the LLE, respond to a 2020 Nature paper that used machine learning techniques to study the liquid-liquid phase transitions of dense hydrogen from an insulating liquid to a liquid metal.