Path integral Monte Carlo simulations of dense carbon-hydrogen plasmas
arXiv:1708.07246 · doi:10.1063/1.5001208
Abstract
Carbon-hydrogen plasmas and hydrocarbon materials are of broad interest to laser shock experimentalists, high energy density physicists, and astrophysicists. Accurate equations of state (EOS) of hydrocarbons are valuable for various studies from inertial confinement fusion (ICF) to planetary science. By combining path integral Monte Carlo (PIMC) results at high temperatures and density functional theory molecular dynamics (DFT-MD) results at lower temperatures, we compute the EOS for hydrocarbons at 1473 separate ()-points distributed over a range of compositions. These methods accurately treat electronic excitation and many-body interaction effects and thus provide a benchmark-quality EOS that surpasses that of semi-empirical and Thomas-Fermi-based methods in the warm dense matter regime. By comparing our first-principles EOS to the LEOS 5112 model for CH, we validate the specific heat assumptions in this model but suggest that the Grueneisen parameter is too large at low temperature. Based on our first-principles EOS, we predict the Hugoniot curve of polystyrene to be 2-5% softer at maximum compression than that predicted by orbital-free DFT and SESAME 7593. By investigating the atomic structure and chemical bonding, we show a drastic decrease in the lifetime of chemical bonds in the pressure interval of 0.4-4 megabar. We find the assumption of linear mixing to be valid for describing the EOS and the shock Hugoniot curve of the dense, partially ionized hydrocarbons under consideration. We make predictions of the shock compression of glow-discharge polymers and investigate the effects of oxygen content and C:H ratio on their Hugoniot curve. Our full suite of first-principles simulation results may be used to benchmark future theoretical investigations pertaining to hydrocarbon EOS, and should be helpful in guiding the design of future gigabar experiments.
arXiv comment14 pages, 13 figures, 1 table;
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