A machine learning methodology for investigating the liquid–liquid transition of hydrogen under high-pressure

Shao Li, Chuanguo Zhang, Xianlong Wang and Zhi Zeng
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Shao Li: Key Laboratory of Materials Physics, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, P. R. China†University of Science and Technology of China, Hefei 230026, P. R. China
Chuanguo Zhang: Key Laboratory of Materials Physics, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, P. R. China
Xianlong Wang: Key Laboratory of Materials Physics, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, P. R. China†University of Science and Technology of China, Hefei 230026, P. R. China
Zhi Zeng: Key Laboratory of Materials Physics, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, P. R. China†University of Science and Technology of China, Hefei 230026, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 12, 1-11

Abstract: Due to its unique properties such as superconductivity and superfluidity, high-pressure properties of hydrogen attract a lot of attention. However, the Liquid–Liquid Transition (LLT) of hydrogen under high-pressure and high-temperature is of particular significance for understanding its metallization. We propose a data-driven machine learning approach based on the density functional theory data to fit the potential energy surface into a deep neural network form. This method overcomes the simulation scale limitations of first-principles approaches to investigate the dissociation behavior of hydrogen molecules under high pressure. Our findings reveal an LLT curve exhibiting a first-order continuous change along with transition zone corresponding to hydrogen molecule dissociation. This study offers valuable insights into the LLT phenomenon and the metallization of hydrogen under high pressure.

Keywords: High-pressure; machine-learning; hydrogen; liquid–liquid transition (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501523

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