Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo
Journal Article
·
· Physical Review Letters
- Harbin Inst. of Technology, Heilongjiang (China); University of Illinois at Urbana-Champaign
- Flatiron Inst., New York, NY (United States); Univ. of Illinois, Urbana, IL (United States)
- Univ. of Illinois, Urbana, IL (United States)
- Univ. Grenoble Alpes (France)
- Univ. of L’Aquila (Italy)
Here, we survey the phase diagram of high-pressure molecular hydrogen with path integral molecular dynamics using a machine-learned interatomic potential trained with quantum Monte Carlo forces and energies. Besides the HCP and C2/c–24 phases, we find two new stable phases both with molecular centers in the Fmmm–4 structure, separated by a molecular orientation transition with temperature. The high temperature isotropic Fmmm–4 phase has a reentrant melting line with a maximum at higher temperature (1450 K at 150 GPa) than previously estimated and crosses the liquid-liquid transition line around 1200 K and 200 GPa.
- Research Organization:
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; NSF of China
- Grant/Contract Number:
- SC0020177; AC05-00OR22725
- OSTI ID:
- 1958013
- Journal Information:
- Physical Review Letters, Journal Name: Physical Review Letters Journal Issue: 7 Vol. 130; ISSN 0031-9007
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
08 HYDROGEN
75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
artificial neural networks
compressed hydrogen
deep learning
diffusion quantum Monte Carlo
electronic structure
equations of state
first-principles calculations
high pressure
hydrogen
machine learning
molecular dynamics
path-integral Monte Carlo
path-integral methods
phase diagrams
phase transitions
quantum monte Carlo
75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
artificial neural networks
compressed hydrogen
deep learning
diffusion quantum Monte Carlo
electronic structure
equations of state
first-principles calculations
high pressure
hydrogen
machine learning
molecular dynamics
path-integral Monte Carlo
path-integral methods
phase diagrams
phase transitions
quantum monte Carlo