Path-integral simulations of solid and liquid atomic hydrogen
Ly, Kevin Kim
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https://hdl.handle.net/2142/124476
Description
Title
Path-integral simulations of solid and liquid atomic hydrogen
Author(s)
Ly, Kevin Kim
Issue Date
2023-11-30
Director of Research (if dissertation) or Advisor (if thesis)
Ceperley, David M
Doctoral Committee Chair(s)
Schleife, André
Committee Member(s)
Lorenz, Virginia O
Gammie, Charles F
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Quantum Monte Carlo
atomic hydrogen
machine learning
lattice dynamics
Abstract
For decades, the characterization of bulk hydrogen and its phases has challenged experimentalists and theorists alike. Solid atomic hydrogen, which requires extremely high pressures and has yet to be produced in the lab, seems just within reach. Accurate calculations of the solid phase are necessary to inform the next generation of experiments. For the theorist, hydrogen’s unique and erratic behavior requires novel and powerful techniques in simulation.
We calculated the phonons of solid atomic hydrogen with reptation quantum Monte Carlo (RQMC). This is the first phonon calculation of this kind, made possible with a simple trick that we devised. We also simulated the lattice dynamics of LaH10, an analogue of solid atomic hydrogen which displays high- temperature superconductivity, with path-integral molecular dynamics. The resolution of our simulations allowed us to identify an intrinsic distortion of the superconducting structure which was previously thought to be extrinsic. Further investigation of this structural transformation also provided some insight into the design of hydrogen-rich superconductors. The improved resolution was enabled by the development of a machine-learned model that learned ab-initio calculations.
Finally, we simulated the melting of solid atomic hydrogen. By combining RQMC and thermodynamic path-integral Monte Carlo, we believe that our results are the most accurate to date. Once again, these simulations were made possible by a machine-learned model. We found the melting line to be higher than previously suggested, though still below the projected superconducting temperature. We also found a clear decrease in the melting line with increasing pressure, reviving hopes that the liquid may be stable down to very low temperatures.
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