Liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential
Year: 2025
Type: article
Abstract: We simulate high-pressure hydrogen in its liquid phase close to molecular dissociation using a machine-learned interatomic potential. The model is trained with density functional theory forces and energies, with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. We show that an accurate NequIP model, an E(3)-equivariant neural network potential, accurately reproduces the phase transition present in PBE. Moreover, the computational efficiency of this model allows for substantially longer molecular-dynamics trajectories, enabling us to perform a finite-size scaling analysis to distinguish between a crossover and a true first-order phase transition. We locate the critical point of this transition, the liquid-liquid phase transition, at 1200-1300 K and 155-160 GPa, a temperature lower than most previous estimates and close to the melting transition.
Source: Physical review. E
Authors: Mathieu Istas, Scott Jensen, Yubo Yang, Markus Holzmann, Carlo Pierleoni, David M. Ceperley
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
Funders: State of Illinois, National Science Foundation, U.S. Department of Energy, Simons Foundation, National Centre for Supercomputing Applications, Flatiron Health, European Commission, Ministero dell'Università e della Ricerca, University of Illinois at Urbana-Champaign, Office of Science, Basic Energy Sciences
Awards: Leadership Class Scientific and Engineering Computing: Breaking Through the Limits, G2415155667, FROM ACCURATE CORRELATED QUANTUM SIMULATIONS TO MESOSCOPIC SCALES, G2906069160, G2952167421, G3812701360, FROM ACCURATE CORRELATED QUANTUM SIMULATIONS TO MESOSCOPIC SCALES, G4810716312, Sustained-Petascale In Action: Blue Waters Enabling Transformative Science And Engineering, G4922940137, G5127309730, G5367696895, G5606180237, G6457648290, G8324313409, G972786147
OpenAlex ID: W4409645747
