Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential

  • Heather Wiebe (Creator)
  • Graeme Ackland (Creator)
  • Hongxiang Zong (Creator)

Dataset

Abstract

The hydrogen phase diagram has several unusual features which are well reproduced by density functional calculations. Unfortunately, these calculations do not provide good physical insights into why those features occur. Here, we present a fast interatomic potential, which reproduces the molecular hydrogen phases: orientationally disordered Phase I; broken-symmetry Phase II, efficiently-packed Phase III and reentrant melt curve. The H2 vibrational frequency drops at high pressure because of increased coupling between neighbouring molecules, not bond weakening. Liquid H2 is denser than coexisting close-packed solid at high pressure because the favored molecular orientation switches from quadrupole-energy-minimizing to steric-repulsion-minimizing. The latter allows molecules to get closer together, without the atoms getting closer, but cannot be achieved within in a close-packed layer due to frustration. A similar effect causes negative thermal expansion. At high pressure, rotation is hindered in Phase I, such that it cannot be regarded as a molecular rotor phase.

This dataset contains DFT trajectory data used for fitting the machine learned interatomic potential.

Data Citation

Zong, Hongxiang; Wiebe, Heather; Ackland, Graeme J. (2020). Understanding high pressure molecular hydrogen with a hierarchical machine-learned potential, [dataset]. University of Edinburgh. School of Physics & Astronomy. https://doi.org/10.7488/ds/2874.
Date made available9 Jul 2020
PublisherEdinburgh DataShare

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