The liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential

TL;DR

This study uses ab initio simulations and a machine-learned neural network potential to accurately analyze the liquid-liquid phase transition of hydrogen, locating its critical point at lower temperatures and pressures than previously estimated.

Contribution

The paper introduces an E(3)-equivariant neural network potential trained on DFT data, enabling detailed analysis of hydrogen's LLPT and its critical point with higher efficiency.

Findings

Critical point of LLPT at 1200-1300 K and 155-160 GPa

NequIP model accurately reproduces phase transition

Longer MD trajectories enable finite-size scaling analysis

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 (DFT) 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 (FSS) 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 (LLPT), at 1200-1300 K and 155-160 GPa, a temperature lower than most previous estimates and close to the melting transition.