Carbon-hydrogen plasmas and hydrocarbon materials are of broad interest to laser shock experimentalists, high energy density physicists, and astrophysicists. Accurate equations of state (EOSs) of hydrocarbons are valuable for various studies from inertial confinement fusion to planetary science. By combining path integral Monte Carlo (PIMC) results at high temperatures and density functional theory molecular dynamics results at lower temperatures, we compute the EOSs for hydrocarbons from simulations performed at 1473 separate (ρ, T)-points distributed over a range of compositions. These methods accurately treat electronic excitation effects with neither adjustable parameter nor experimental input. PIMC is also an accurate simulation method that is capable of treating many-body interaction and nuclear quantum effects at finite temperatures. These methods therefore 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 Grüneisen parameter is too large at low temperatures. Based on our first-principles EOSs, we predict the principal Hugoniot curve of polystyrene to be 2%-5% softer at maximum shock compression than that predicted by orbital-free density functional theory and SESAME 7593. By investigating the atomic structure and chemical bonding of hydrocarbons, we show a drastic decrease in the lifetime of chemical bonds in the pressure interval from 0.4 to 4 megabar. We find the assumption of linear mixing to be valid for describing the EOS and the shock Hugoniot curve of hydrocarbons in the regime of partially ionized atomic liquids. We make predictions of the shock compression of glow-discharge polymers and investigate the effects of oxygen content and C:H ratio on its Hugoniot curve. Our full suite of first-principles simulation results may be used to benchmark future theoretical investigations pertaining to hydrocarbon EOSs and should be helpful in guiding the design of future experiments on hydrocarbons in the gigabar regime.

Accurate equations of state (EOSs) of materials under various temperature and pressure conditions are of fundamental importance in earth and planetary science, astrophysics, and high energy density physics.1 These applications require that we understand matter that is partially ionized and strongly coupled. This includes conventional condensed matter (TTFermi), warm dense matter (TTFermi), and weakly coupled plasmas (TTFermi and potential energykinetic energy). At high temperatures (>102 eV) and near-ambient densities, both electrons and nuclei can generally be treated as ideal gases due to complete ionization of atoms into a perfect plasma state. At slightly lower temperatures, the Debye-Hückel model may be used to treat weak interactions within a screening approximation. Materials in condensed forms are characterized by both strong coupling and degeneracy effects and thus require sophisticated quantum many-body methods for their description. However, very good progress can be made with average-atom methods such as the average-atom Thomas-Fermi theory and average-atom Kohn-Sham Density Functional Theory (DFT), in which isolated ions are embedded within a spherically symmetric electron liquid. Kohn-Sham-DFT average-atom methods resolve distinct electronic shells of atoms, just as in conventional applications of quantum theory to isolated atoms, but they do not account for directional bonding between atoms and therefore fail at low temperatures. Thomas-Fermi and more general orbital-free (OF) DFT approaches do not account for electronic shell effects and are thus unable to properly describe partially ionized plasmas; as such, they provide a less than satisfactory description of warm dense matter.

EOS models [e.g., of the quotidian equation of state (QEOS) type2] treating wide ranges of density and temperature and databases that house them (e.g., SESAME3 and LEOS) make heavy use of average-atom theories for electronic excitations, ad hoc interpolation formulas which mediate the evolution of the ionic specific heat from low-T to the high-T ideal gas limit, and semi-empirical models which allow for the fitting of experimental results near ambient conditions. The efficacy of these EOS models is questionable precisely in the regimes currently probed in dynamic compression experiments reaching gigabar (Gbar) pressures, in which it is expected that atoms are significantly (though not fully) ionized by both temperature and pressure. Clearly, this regime is in need of more sophisticated theoretical treatments which more fully account for detailed electronic structure and many-body effects.

First-principles molecular dynamics (MD) based on DFT is widely used for calculating the atomic structure, the EOS, and other electronic and ionic properties of materials at relatively low temperatures, where the ionization fraction is small. In DFT-MD, the nuclei are usually treated as classical particles whose motion follows Newton’s equation of motion, whereas the potential field is determined by solving a single-particle mean-field equation self-consistently using DFT. This method naturally includes the anharmonic terms of nuclear vibrations and is usually a good approximation for systems with heavy elements and is widely used for simulations of earth and planetary materials.4–9 For light elements, zero-point motion cannot be neglected,10,11 which makes important contributions to the energy that may alter relative phase stability. At temperatures above 100 eV, DFT-MD of the Kohn-Sham variety becomes computationally intractable due to the considerable number of high-lying single-electron states that need to be included. It is also practically limited by the use of pseudopotentials that reduce computational costs by freezing inner-shell electrons within ionic cores that tend to overlap between neighboring atoms if the system is at significant compression, leading to errors.

Path integral Monte Carlo (PIMC)12,13 offers an approach to directly solve the many-body Schrödinger equation in a stochastic way. It typically treats nuclei and electrons as quantum paths that evolve in imaginary time and obtains the energy and other properties of a system by solving the thermal density matrix and computing thermodynamic averages within the sampled ensembles. For fermionic systems, a suitable nodal structure is required to restrict the sampling space in order to avoid the sign problem that arises from antisymmetry of the many-body density matrix. Accuracy of the method has been demonstrated by early work on fully ionized hydrogen14,15 and helium16 plasmas. In the past five years, developments by extending free-particle nodes17 or implementing localized orbitals18 to construct the nodes have enabled PIMC studies of a series of heavier elements and compounds.17–28 These studies have applied PIMC to EOS calculations at temperatures ranging from a few hundred million K to as low as 2.5×105 K. For first- and second-row elements, PIMC and DFT-MD simulations produce consistent EOS results at intermediate temperatures.

While computer simulations with classical nuclei provide sufficiently accurate predictions for a wide range of thermodynamic conditions, at low temperature, nuclear quantum effects (NQEs) often play an important role in predicting observed phenomena.29 One typically compares the inter-particle spacing to the thermal de Broglie wavelength (1/mkBT) that is inversely proportional to the square root of mass and temperature, and therefore, cold and low-Z materials are strongly influenced by NQEs. Often such effects are referred to as zero-point motion. In solids, zero-point effects are typically studied with lattice dynamics calculations that derive the spectrum of vibrational eigenmodes within the quasi-harmonic approximation. Such calculations rely on the second derivative energy with respect to the nuclear positions that can be derived with theoretical methods of various levels of accuracy ranging from classical force fields,30 DFT,31,32 and in principle also with quantum Monte Carlo calculations.33 It is difficult, however, to introduce anharmonic effects accurately into the lattice dynamics approach.34 Anharmonic effects are important for temperatures above the Debye temperature, in particular near melting, but also at very low temperature in materials that are rich in hydrogen or helium. Because of their small mass, the nuclear wavefunctions often spread into the anharmonic regions of the confining potential even in the ground state so that anharmonic effects can no longer be neglected.

Path integral methods35 can accurately incorporate NQEs and anharmonic effects at all temperatures and can describe solids as well as liquids.36,37 An efficient approach, that has been devised to pursue NQE problems, is to combine the path integral method for nuclei with other electronic structure methods, such as DFT or quantum Monte Carlo, to efficiently describe the forces between the nuclei. Path integral molecular dynamics38 and coupled electron ion Monte Carlo39 are two common techniques that employ this combined approach.40 A few examples include transport properties41 and phase transitions in solid11,42 and liquid10,43,44 hydrogen, helium,16,45 hydrogen-bonding in water46–49 and on metal surfaces,50 solid water-ice phases,51,52 phonon dispersion in diamond,53 and energy barriers for small molecules.54,55

In this work, we employ the path integral methods to study partially and highly excited electrons. Even though the temperatures under consideration are quite high (100 eV), fermionic effects are crucial to characterize the electronic states accurately. The occupation of bound electronic states affects the motion of the nuclei through Pauli exclusion. At high density, the motion of the nuclei deforms the shape of the electronic orbitals. Thus, a fully self-consistent approach is needed for materials in the warm dense matter (WDM) regime.

We apply PIMC simulations with free-particle nodes and DFT-MD calculations to study carbon-hydrogen compounds. Hydrocarbons are currently in use as ablator materials in dynamic compression and inertial confinement fusion (ICF) experiments.56–60 Materials such as polystyrene and glow-discharge polymer (GDP) have been a strong point of interest and extensively studied by both theorists and experimentalists.61–83 Recently, laser shock experiments at the National Ignition Facility (NIF)84–89 and the OMEGA Laser facility90 have extended the ablation pressure in CH to the Gbar range.91 Calculations employing DFT-MD or OF-DFT have been performed to study the EOS of hydrocarbon materials.81,92–98 These theoretical studies predicted shock Hugoniot curves that agree well with experiments at low temperatures. At high temperatures, the Kohn-Sham DFT is unfeasible while OF-DFT works efficiently but its predictions are yet to be tested by other theories23 and experiments. We expect the PIMC and DFT-MD simulations of this study to produce predictions for the EOS of hydrocarbons which are sufficiently accurate to be used as benchmarks for both the construction of wide-range EOS models and the further investigation of hydrocarbon EOSs with less computationally expensive simulation methods. Ultimately, our predictions may prove useful for the design and interpretation of dynamic compression and ICF experiments in the future.

The paper is organized as follows: Sec. II introduces the details of our simulation methods. Section III presents our EOS results, the shock Hugoniot curves, and comparisons with other theories, models, and experiments. Section IV discusses the structural evolution of hydrocarbons and the shock compression of GDPs and related materials. We conclude in Sec. V.

We use the C++ universal path integral deamon (CUPID) code99 for our PIMC simulations within the fixed-node approximation.100 We treat the nuclei as quantum particles, even though the kinetic energy is much larger than the zero-point contribution to the total energy of the system at high temperatures (T106 K = 87 eV) considered here. Electrons are treated as fermions. Their quantum paths are periodic in the imaginary time interval, 0tβ=1/kBT (kB is the Boltzmann constant), but the paths of electrons with the same spin may be permuted as long as they do not violate the nodal restriction.100 Following our previous work on hydrogen,14,15,37,101–106 helium,107,108 and carbon,17,19 we use free-particle nodes to constrain the sampling space by restricting the paths to positive regions of the density matrix of ideal fermions. Coulomb interactions between all pairs of particles are introduced via pair density matrices.109,110 The pair density matrices are evaluated at an imaginary time interval of 1/1024 Hartree−1 (Ha−1), while the nodal restriction is enforced in steps of 1/8192 Ha−1.

For DFT-MD simulations, we use the Vienna Ab initio Simulation Package (VASP).111 We choose the hardest available projected augmented wave (PAW) pseudopotentials112 with core radii of 1.1 and 0.8 bohr for C and H, respectively. All electrons are treated as valence electrons. Exchange-correlation effects are treated within the local density approximation (LDA).113,114 We choose a large plane-wave basis cutoff of 2000 eV, the Γ point to sample the Brillouin zone, and an MD time step of 0.05-0.2 fs, depending on the temperature. MD trajectories are generated in an NVT ensemble and typically consist 2000-10 000 steps to make sure the system is in equilibrium and the energies and pressures are converged. The temperature is controlled with a Nosé thermostat.115 All VASP energies are shifted by −37.4243 Ha/C and −0.445 893 Ha/H, to put DFT-MD energies on the same scale as our all-electron PIMC energies. These values are determined by performing all-electron single-atom calculations using the OPIUM code.116 

We study six different compositions by simulating C20H10, C18H18, C16H24, C14H28, C12H36, and C10H40 in a cubic cell at temperatures between 106 and 1.3 ×108 K using PIMC and 6.7 ×103 and 106 K using DFT-MD methods. For C:H = 1:1, we use larger cells with four times as many atoms at the lower temperatures of 6.7 ×103-2.5 ×105 K in order to minimize finite-size effects, which are expected to be larger at low temperatures.17 In order to maximize the computational efficiency of the large-cell simulations, we freeze the 1s2 electrons of carbon in the pseudopotential core without losing accuracy because the temperatures are much lower than the ionization energy (392 eV for 1s2 of C).117 We consider a grid of nine isochores for each hydrocarbon system chosen so that the pressure ranges for each composition are similar; this results in densities for CH between (2 and 12) ×ρambient (see Fig. 1). These conditions are both relevant to dynamic compression experiments and well within the range in which Kohn-Sham DFT-MD simulations with pseudopotentials are feasible.

FIG. 1.

Pressure-temperature conditions considered in our PIMC and DFT-MD simulations of hydrocarbons. For clarity, only the EOS data for C2H and the Hugoniot curves of C2H, CH, and CH2 are shown. The Hugoniot curves are obtained by setting the initial density to 1.12, 1.05, and 0.946 g/cm3 for C2H, CH, and CH2, respectively. The shaded region denotes the approximate area that the EOS of all hydrocarbons studied in this work fall in.

FIG. 1.

Pressure-temperature conditions considered in our PIMC and DFT-MD simulations of hydrocarbons. For clarity, only the EOS data for C2H and the Hugoniot curves of C2H, CH, and CH2 are shown. The Hugoniot curves are obtained by setting the initial density to 1.12, 1.05, and 0.946 g/cm3 for C2H, CH, and CH2, respectively. The shaded region denotes the approximate area that the EOS of all hydrocarbons studied in this work fall in.

Close modal

We also investigate the validity of the linear mixing approximation for estimating the EOS of various hydrogen-carbon mixtures. In this method, the energy and the density of the mixture are obtained with the isobaric, isothermal additive volume assumption via Vmix(P, T) = fCVC(P, T) + fHVH(P, T) and Emix(P, T) = fCEC(P, T) + fHEH(P, T), where fC = nC/(nC + nH) and fH = nH/(nC + nH) are the mixing ratios, Vmix, VC, VH are volumes per atom for the mixture, pure carbon, and pure hydrogen, respectively (and likewise for the internal energies, E). E(T, P) and V(T, P) of the pure species are constructed using bi-variable spline fitting over the ρ-T space spanned by the EOS of pure hydrogen and pure carbon. The shock Hugoniot curves derived with simulations of the fully interacting system can be compared to that obtained with the linear mixing approximation.

Figure 2 shows the calculated EOS for C2H along nine isochores. A complete list of EOS data for all hydrocarbons (C2H, CH, C2H3, CH2, CH3, CH4) in this study are in the supplementary material. The internal energies and pressures from PIMC calculations agree with predictions of the Debye-Hückel model at temperatures above 4 ×106 K and with the Fermi electron gas theory (wherein both ions and electrons are treated as uniform-density free Fermi gases) above 8 ×106 K, which is higher than the 1s1 ionization energy (489.99 eV or 5.7 ×106 K)117 of carbon. PIMC results show excellent agreement with DFT-MD at 106 K, with differences typically less than 1 Ha/carbon in internal energy and 3% in pressure. We have therefore constructed a consistent first-principles EOS table for warm dense hydrocarbons, over a wide density-temperature range of 1.4-13.5 g/cm3 and 6.7×103-1.3×108 K and C:H = 2:1-1:4. The good consistency of PIMC with DFT-MD and the other high-temperature theories indicates that it is reliable to use the free-particle nodes in PIMC for temperatures as low as 106 K and PAW pseudopotentials and zero-temperature exchange-correlation functionals in DFT-MD up to 106 K.

FIG. 2.

Internal energies and pressures of CH along isochores. Corresponding results of the Fermi-gas and the Debye-Hückel theories are plotted for comparison. PIMC predicts consistent results with DFT-MD at 106 K PIMC and agrees with ideal Fermi gas and Debye-Hückel models at above 8 × 106 K. As temperature decreases, the ideal Fermi gas model significantly overestimates the energy and the pressure because of the neglect of interactions. The Debye-Hückel model improves over the Fermi-gas model for temperatures down to 3 × 106 K but leads to low pressures and energies at lower temperatures, at which electron-nucleus coupling gets stronger and the screening approximation breaks down. DFT-MD and OF-DFT results from Ref. 96 are shown with green diamonds for comparison. Error bars of the data are much smaller than the size of the symbols. Different isochores have been shifted apart for clarity.

FIG. 2.

Internal energies and pressures of CH along isochores. Corresponding results of the Fermi-gas and the Debye-Hückel theories are plotted for comparison. PIMC predicts consistent results with DFT-MD at 106 K PIMC and agrees with ideal Fermi gas and Debye-Hückel models at above 8 × 106 K. As temperature decreases, the ideal Fermi gas model significantly overestimates the energy and the pressure because of the neglect of interactions. The Debye-Hückel model improves over the Fermi-gas model for temperatures down to 3 × 106 K but leads to low pressures and energies at lower temperatures, at which electron-nucleus coupling gets stronger and the screening approximation breaks down. DFT-MD and OF-DFT results from Ref. 96 are shown with green diamonds for comparison. Error bars of the data are much smaller than the size of the symbols. Different isochores have been shifted apart for clarity.

Close modal

In comparison with a recent first-principles study96 that employs DFT-MD with the Perdew-Burke-Ernzerhof (PBE)118 exchange-correlation functional at temperatures below the Fermi temperature TFermi, the P-T and E-T curves coincide with LDA curves in this work. This indicates that the EOS does not significantly depend on the form of the exchange-correlation functional in the temperature interval under consideration. At 106 K and higher temperatures, the energy of Ref. 96 is different from PIMC predictions of this work while the pressure differences are small. This is associated with underestimation of the compression maximum by OF-DFT that is used in Ref. 96. More details on this will be discussed in Sec. III C.

An important aim of this work is to produce benchmarking EOS predictions that will act as constraints in the future construction of EOS models for hydrocarbons which span wide ranges of density and temperature, well beyond that where experimental data is available. It is therefore interesting to compare the details of existing EOS models, e.g., CH, to the first-principles predictions in this work. The most recent such model for polystyrene (CH) constructed at the Lawrence Livermore National Laboratory is LEOS-5112, closely related to LEOS-5400,119 currently used as the EOS model of choice for GDP in inertial confinement fusion simulations where that material is employed as an ablator. EOS models such as these assume that the free energy is decomposable into separate ionic and electronic excitation terms. While somewhat justified due to the large ion/electron mass ratio, it is important whenever possible to compare to EOS predictions from ab initio methods (such as PIMC) that do not make this assumption.

The LEOS-5400 EOS model119 was originally constructed to represent the non-stoichiometric carbon-hydrogen-oxygen composition of GDP and to reproduce both Hugoniot and off-Hugoniot measurements (specifically, shock-and-release from an interface with deuterium).83 More recently, LEOS-5112 was developed to facilitate comparison with experiments and simulations on the stoichiometric material, CH. This CH model closely follows the parameters used to make GDP LEOS-5400.

The cold curve, E(V, T = 0), was based on a constant-pressure mix of the corresponding Thomas-Fermi cold curves for pure C and pure H, with a bonding correction2 to set the density at 1.049 g/cm3 and the bulk modulus at 0.9 GPa at a temperature of 20 K. This relatively high bulk modulus was softened by using a break point120 that changes the cold curve energy by δE = Au3/(B + u3) for u1, where u = ρ/ρ0, A = −8 kJ/g, and B = 3.0. A corresponding change was also made to the cold curve pressure. The relatively high bulk modulus value, together with the break point, reproduces both the initial equilibrium conditions of the material (at cryogenic conditions) and the higher-temperature behavior above 50 GPa, which is higher than the graphite-diamond transition pressure along the Hugoniot (15-25 GPa).

The terms in the free energy accounting for ionic excitations (ion-thermal) were modeled using both a Debye-Grüneisen model and a dissociation model. The Debye model used a Debye temperature of 650 K at equilibrium density and an ion-thermal Grüneisen γ at this point of 0.99. This value of γ was originally chosen to give the experimentally observed thermal expansion of GDP between 20 K and room temperature. The Grüneisen parameter was kept constant from ρ0 up to a density of 2.7 g/cm3, at which point it was gradually decreased to 0.81 at 10 g/cm3 and 0.5 at very high density. For densities below 1.049 g/cm3, the Grüneisen gamma reduces gradually to the ideal-gas value of 2/3. The variation in the Debye temperature is computed from this γ(ρ) function.2 The dissociation model adds an additional contribution to the free energy which models the dissociation of a dimer.120 The main purpose of this term is to model chemical dissociation in a simplified way, as if it were due to diatomic molecular dissociation. In the GDP model, this extra flexibility allowed simultaneous fits to both Hugoniot data and off-Hugoniot release data. This same model was retained in LEOS-5112 without modification. The model includes a dimer dissociation energy of 0.7 eV and a nominal rotational temperature of 20 K.

The liquid contribution to the ion-thermal free energy in LEOS-5112 (and LEOS-5400) is given by a Cowan model2 with an exponent of 1/3,

CVion=3kB2+3kB2Tm(ρ)T1/3.
(1)

This ensures that the ionic contribution to the specific heat decays from 3kB/ion to the ideal gas value of 1.5 kB/ion as T. Tm(ρ) is taken to be the melt temperature, determined from the Lindemann relation.2 Since CH dissociates before melting, it is necessary to assume a value for the melt temperature at ambient pressure. The value used here, Tm = 513.15 K, is the same as that used in LEOS-5400 for GDPs.

The electronic excitation contribution to the free energy of LEOS-5112 comes from the Purgatorio atom-in-jellium calculations121 for carbon and hydrogen. The Purgatorio cold curve is replaced by a Thomas-Fermi cold curve, and some data adjustment is done in the low temperature region around equilibrium density to guarantee monotonicity in the pressure. These tables are then mixed using a constant-pressure, constant-temperature additive volume mix procedure. The resulting cold curve is then subtracted to yield the electron-thermal contribution used in the EOS. This includes the effects of the shell structure, as well as the relativistic effects at very high temperatures.

Figure 3 shows the specific heat at constant volume, CV, for CH at a density of 3.15 g/cm3. The black curve is the result of calculating (E/T)V directly from the DFT-MD (for T106 K) and PIMC (for T > 106 K) internal energies, by fitting a cubic spline to our 20 E(T) points at this density and differentiating the smooth spline function. At the highest T, this asymptotes to 6.75 kB/atom, which is the required value from equipartition, assuming complete ionization. There is a notable peak in this curve just above 106 K. The red curve shows CVelectron from LEOS-5112, obtained as described above from the Kohn-Sham-DFT average-atom Purgatorio model.121 Though the peak is in a slightly different position than that seen in the black curve, this suggests that (a) the peak in CV at T = 106 K arises from electronic ionization, and (b) this feature is modeled well by the comparatively simple Purgatorio treatment (which neglects, e.g., directional bonding). The temperature interval of the peak and related information presented in Refs. 23 and 28 suggest that its appearance results primarily from the ionization of the 1s electron shell of carbon. The green curve shows the black curve minus the red curve, which is an estimation of the ionic contribution to CV, assuming the perfect additivity of electronic and ionic components together with the use of Purgatorio for the electronic piece. Note that it approaches the required value of 1.5 kB/atom at high-T while rising slowly as T decreases, ultimately rising rapidly to a value of 3kB/atom at the lowest T (a value of 2.9 kB/atom was predicted for CH1.36 in Ref. 83). This rapid rise is reminiscent of behavior seen in DFT-MD calculations of pure carbon (see Fig. 9 of Ref. 19) where CV was shown to drop quickly from 3kB/atom with increasing T over the range 2 ×104-8 ×104 K. Above a few times 104 K, the general behavior of the green curve is tracked rather well by the blue curve, which shows CVion from LEOS-5112 at this density, given by the form in Eq. (1). With the exception of the small discrepancies at the lowest T of Fig. 3, the general agreement shown here provides validation for the manner in which the specific heat is modeled in LEOS-5112, indicating that at least the T-dependent part of E is treated reasonably well. This is noteworthy, for the work on pure carbon19 demonstrated that the model of Eq. (1) was suspect even for T as high as 106 K. The behavior shown in Fig. 3 is reproduced in our other energy isochores as well.

FIG. 3.

CV for CH at a density of ρ = 3.15 g/cm3. The black curve is CV as extracted from a spline fit of our discrete E(T) predictions from first-principles simulations at this density. See the text for descriptions of the other curves.

FIG. 3.

CV for CH at a density of ρ = 3.15 g/cm3. The black curve is CV as extracted from a spline fit of our discrete E(T) predictions from first-principles simulations at this density. See the text for descriptions of the other curves.

Close modal

We further study the density dependence of CV(T) using LEOS 5112 in order to better understand the ionization process of CH. In Fig. 4, we show a comparison of the CV profiles over a much wider density range from 10−5 to 100 g/cm3 than we can access with existing simulation methods. For densities 0.1 g/cm3, we find two peaks: a narrow one above 3×105 K corresponding to the ionization of the K shell (1s state) and a wide one at 105 K due to the L shell of the carbon atoms. As the density increases, both peaks widen and shift to higher temperatures. These changes are due to the complex interplay of three effects: variations in the orbital shapes and an increase in size relative to the interatomic distances, a depletion of the 1s Fermi occupancy due to the temperature increase, and changes in the 1s binding energy due to variations in the screening effects. The K shell peak in the heat capacity is predicted to disappear at 100 g/cm3, even though the 1s orbital is still bound at this density, and is not fully pressure ionized until a density of over 500 g/cm3. The suppression of the peak is a result of changes in the 1s binding energy as the 1s state is thermally depopulated. As the Fermi function reduces the occupancy of the orbital with increasing temperature, the binding energy of the 1s state increases due to a reduction in screening. This, in turn, raises the temperature required to depopulate the state, which results in a broadening of the heat capacity peak, leading to its apparent disappearance for the 100 g/cm3 case. This reduction-in-screening effect is present at all densities, but it is only at high enough densities since the heat capacity peak is shifted to sufficiently high temperatures so that it becomes undetectable when broadened. For densities less than 0.01 g/cm3, we also identified a shoulder in the L-shell peak at approximately 3 ×104 K, which we attribute to ionization effects of the hydrogen atoms because this shoulder is absent from the high-density carbon Purgatorio-basedtable.

FIG. 4.

CV(T) of CH from LEOS 5112 at 10−5,10−4,10−3,10−2, 0.1, 1, 10, and 100 g/cm3.

FIG. 4.

CV(T) of CH from LEOS 5112 at 10−5,10−4,10−3,10−2, 0.1, 1, 10, and 100 g/cm3.

Close modal

While the temperature dependence of the internal energy of LEOS-5112 is therefore in quite good accord with that of our first-principles results throughout a wide range of density and temperature, the pressure of this model shows more significant discrepancies. Figure 5 shows isochores of P for four densities, ρ = 2.1, 3.15, 4.2, and 5.25 g/cm3. At the lower-T, the pressures of the model are systematically too high; near-perfect agreement is only seen at much higher temperature, approaching the ideal gas limit. This suggests that the Grüneisen γ is too large in LEOS-5112 since this quantity does not enter CV discussed earlier. Still another possibility is that the cold curve of LEOS-5112 can benefit from slight modification, though our current inclination is to reinvestigate the combined Debye-Grüneisen and dissociation models pertaining to the ionic excitation contribution. Work to improve the EOS models of hydrocarbons such as CH using these first-principles simulations is ongoing. In the following Sec. III C, LEOS-5112 and LEOS-5400 are discussed again, in the context of predicted Hugoniot curves.

FIG. 5.

Total pressure isochores for CH at densities ρ = 2.1, 3.15, 4.2, and 5.25 g/cm3 (in the sequence of bottom to top). The black symbols are the results of our DFT-MD (T106 K) and PIMC (T > 106 K) simulations. The blue curves are the corresponding pressure isochores from LEOS-5112.

FIG. 5.

Total pressure isochores for CH at densities ρ = 2.1, 3.15, 4.2, and 5.25 g/cm3 (in the sequence of bottom to top). The black symbols are the results of our DFT-MD (T106 K) and PIMC (T > 106 K) simulations. The blue curves are the corresponding pressure isochores from LEOS-5112.

Close modal

The locus of final states characterized by (E, P, V) accessible via a planar one-dimensional shock satisfies the Rankine-Hugoniot energy equation,122 

H(T,ρ)=(EE0)+12(P+P0)(VV0)=0,
(2)

where (E0, P0, V0) are the variables characterizing the initial (pre-shocked) state. This allows for the determination of the P-V-T Hugoniot curve with bi-variable spline fitting of the EOS data (E and P on a grid of T and V) in Sec. III A.

We thus obtain the principal Hugoniot curves of hydrocarbons28 and represent those of CH (assuming ρ0 = 1.05 g/cm3 and T0 = 300 K) in a pressure-density plot (Fig. 6) together with experimental measurements at low pressures,69,70,74,75 OF-DFT simulations,96 and the Purgatorio-based LEOS 5112 (described earlier), a quantum semiempirical model (QSEM),98 and the SESAME 75933 models. The DFT-MD predictions of the Hugoniot curve in this work and those of Ref. 96 agree well with experimental measurements, whereas SESAME 7593 deviates from the experimental data at 1-4 TPa. At 0.5 Gbar, we predict CH to reach a maximum compression (ρ/ρ0) of 4.7, which is similar to that predicted by the LEOS 5400 model for GDP, and is higher than that of QSEM,98 LEOS 5112, and OF-DFT96 by 2%-5%; the SESAME 7593 model predicts the maximum compression to be smaller by 7.3% and the corresponding pressure about 17 megabar (Mbar) lower than our first-principles simulations. The compression maximum is originated from the 1s shell ionization of carbon. Since such temperature and pressure conditions correspond to the region at which PIMC works well and complexities such as electronic quantum effects, electronic correlation, and partial ionization are all essentially included in the quantum many-body framework, we expect the predictions in this work to be more reliable than those of semi-empirical models and OF-DFT. We note that the shock Hugoniot curve of CH obtained in this work is in remarkable agreement with that using a recent extended DFT method.123,124 We look forward to accurate experiments in the Gbar regime which test these predictions.

FIG. 6.

Comparison of the shock Hugoniot curve of CH and GDP from first-principles calculations (PIMC and DFT-MD simulations or linear-mixing approximation in this work and OF-DFT and DFT-MD simulations in Ref. 96), EOS models (SESAME 7593,3 QSEM,98 LEOS 5400,119 LEOS 5112), and experiments.69,70,74,75 Black circles are conditions chosen for examining the linear mixing approximation (see Sec. IV A).

FIG. 6.

Comparison of the shock Hugoniot curve of CH and GDP from first-principles calculations (PIMC and DFT-MD simulations or linear-mixing approximation in this work and OF-DFT and DFT-MD simulations in Ref. 96), EOS models (SESAME 7593,3 QSEM,98 LEOS 5400,119 LEOS 5112), and experiments.69,70,74,75 Black circles are conditions chosen for examining the linear mixing approximation (see Sec. IV A).

Close modal

Interestingly, our calculations and several other methods and models (Fig. 6) all have a shoulder along the Hugoniot curve at 4-fold compression. This corresponds to a pressure of 10 TPa and a temperature of 5.7 ×105 K, according to the shock compression analysis of the first-principles EOS data in this work. The origin of this shoulder may be traced back to the start of ionization in the carbon 1s shell. Increasing amounts of carbon 1s electrons are excited at higher temperatures, as is shown in the N(r) plot of Fig. 7. At 106 K, a noticeable amount of excitation can be seen. The ionization fraction of the carbon 1s shell grows considerably as temperature increases even further. Above 8 ×106 K, 1s ionization is nearly complete and the system approaches an ideal plasma. Therefore the Hugoniot curves from different methods and models merge together and reach the ideal Fermi gas limit, which is consistent with the EOS comparisons and discussions in Sec. III A.

FIG. 7.

The average number of electrons N(r) around each carbon or hydrogen nucleus in CH at 3.15 g/cm3 and a series of temperatures obtained from the PIMC simulations in this work. N(r) is calculated via N(r)=e,Iθ(rrerI)/NI, where the sum includes all electron-carbon ion or electron-hydrogen ion pairs and θ represents the Heaviside function. The corresponding profile of the C4+ ionization state, calculated with GAMESS, is shown for comparison.

FIG. 7.

The average number of electrons N(r) around each carbon or hydrogen nucleus in CH at 3.15 g/cm3 and a series of temperatures obtained from the PIMC simulations in this work. N(r) is calculated via N(r)=e,Iθ(rrerI)/NI, where the sum includes all electron-carbon ion or electron-hydrogen ion pairs and θ represents the Heaviside function. The corresponding profile of the C4+ ionization state, calculated with GAMESS, is shown for comparison.

Close modal

In order to better understand the differences between the Hugoniot curves from our simulations and the OF-DFT and DFT-MD studies in Ref. 96, we compare the two components of the Hugoniot function in Fig. 8, i.e., the internal energy term EE0 and the pressure term (P + P0) (VV0)/2, at two different temperatures. At 106 K, data in this work and those of Ref. 96 both rely on DFT-MD simulations; thus, the energy and pressure values are similar. At 4×106 K, the OF-DFT pressure is slightly lower than that given by PIMC and the difference between them grows larger for higher densities, whereas the internal energy from OF-DFT is significantly lower than that of PIMC, resulting in lower densities along the Hugoniot curve. The differences between the Hugoniot curves from the two methods are similar to those found for Si.23 These differences originate from the different treatments of electronic shell ionization effects in the two methods—PIMC is a many-body approach that accurately includes shell effects, while the OF-DFT approach makes use of what is essentially a Thomas-Fermi density functional which is not able to describe the shell structure accurately. Therefore, OF-DFT tends to smooth out ionization features near the compression maximum, leading to a single broad peak instead of the peak-shoulder (for CH) or double-peak (for Si) structures predicted by PIMC.

FIG. 8.

Comparison of the energy and pressure components of the Hugoniot function of CH from this work (solid lines and squares) and from the OF-DFT model96 (dashed lines and diamonds) at two different temperatures. The initial state corresponds to the ambient density of 1.05 g/cm3. The densities at which the energy and pressure terms agree solve Eq. (2) and may be realized in experiments.

FIG. 8.

Comparison of the energy and pressure components of the Hugoniot function of CH from this work (solid lines and squares) and from the OF-DFT model96 (dashed lines and diamonds) at two different temperatures. The initial state corresponds to the ambient density of 1.05 g/cm3. The densities at which the energy and pressure terms agree solve Eq. (2) and may be realized in experiments.

Close modal

Note that zero-point motion has not been considered in our calculation of the reference energy E0 nor in our DFT-MD simulations. To estimate its effect, we approximate125 the harmonic zero-point energy with δE = 9kBΘD(V)/8, where kB is the Boltzmann constant, ΘD(V) is the volume-dependent Debye temperature, and the corresponding pressure δP = 9γkBΘD(V)/8V, where γ is the Grüneisen parameter, and add them to our DFT-MD EOS table of CH. ΘD(V0) and γ are approximated by 140 K and 1.2, respectively, by referring to the values of plastics in the literature.126,127 With this zero-point correction, the compression ratio along the CH Hugoniot curve is reduced by a small amount of 0.01 for temperatures less than 105 K. The maximum correction to our EOS occurs for the highest density of 12ρ0, at which ΘD(V) reaches a high value of 2761.5 K. However, the zero-point motion has a negligible effect (<0.3%) when comparing the EOS from PIMC and DFT-MD at 106 K.

In Ref. 28, we have shown that the isothermal isobaric linear mixing approximation is very reasonable for hydrocarbons under stellar-core conditions. The validity of this approximation can be understood from analysis of the nuclear pair correlation functions, as is shown with the high-temperature (6.7 ×104 K) g(r) profiles in Fig. S2 of Ref. 28 for CH and Fig. 9 for C2H and CH2. At these extreme conditions, no C–H bonds exist (lifetime shorter than 4.4 fs, see Table I), and the non-existence of any peak structure in the pair-correlation function indicates that the system behaves similarly to an ideal yet partially ionized plasma. This explains the efficacy of the ideal linear mixing assumption manifested in the similarity between the Hugoniot curve as calculated from this approximation (using PIMC and DFT-MD EOS for the pure elements19,105) and the Hugoniot curve as calculated from our direct first-principles simulations of the mixtures in question.28 

FIG. 9.

Comparison of the nuclear pair correlation function obtained from DFT-MD for C2H (dark) and CH2 (red) at two different densities for every material and three temperatures. The reference density ρ0 is 1.76 and 2.24 g/cm3 for C2H and CH2, respectively.

FIG. 9.

Comparison of the nuclear pair correlation function obtained from DFT-MD for C2H (dark) and CH2 (red) at two different densities for every material and three temperatures. The reference density ρ0 is 1.76 and 2.24 g/cm3 for C2H and CH2, respectively.

Close modal
TABLE I.

Comparison of chemical bond lifetime τ in fs.

ρ (g/cm3) T (K) P (Mbar) τ C–C τ C–H τ H–H τ C–C–C
2.10  6.7 ×103  0.44  89.62  16.68  4.60  44.12 
2.10  1.0 ×104  0.61  48.77  11.36  4.00  23.92 
3.15  6.7 ×103  1.76  69.72  12.63  4.18  35.67 
3.15  1.0 ×104  2.04  43.13  10.19  3.80  21.72 
3.15  2.0 ×104  2.89  25.63  7.58  3.15  12.93 
3.15  5.0 ×104  5.55  14.63  5.06  2.28  7.55 
3.15  6.7 ×104  7.19  12.44  4.34  2.06  6.58 
3.15  1.3 ×105  13.4  9.50  3.30  1.60  4.88 
3.15  2.0 ×105  22.1  7.47  2.68  1.32  3.85 
3.15  5.1 ×105  60.9  4.70  1.66  0.88  2.47 
ρ (g/cm3) T (K) P (Mbar) τ C–C τ C–H τ H–H τ C–C–C
2.10  6.7 ×103  0.44  89.62  16.68  4.60  44.12 
2.10  1.0 ×104  0.61  48.77  11.36  4.00  23.92 
3.15  6.7 ×103  1.76  69.72  12.63  4.18  35.67 
3.15  1.0 ×104  2.04  43.13  10.19  3.80  21.72 
3.15  2.0 ×104  2.89  25.63  7.58  3.15  12.93 
3.15  5.0 ×104  5.55  14.63  5.06  2.28  7.55 
3.15  6.7 ×104  7.19  12.44  4.34  2.06  6.58 
3.15  1.3 ×105  13.4  9.50  3.30  1.60  4.88 
3.15  2.0 ×105  22.1  7.47  2.68  1.32  3.85 
3.15  5.1 ×105  60.9  4.70  1.66  0.88  2.47 

Figure 10 compares the EOS of CH from interpolation of our first-principles data and that determined by the linear mixing approximation at a series of T, P conditions along the Hugoniot curve (Fig. 6). In comparison to interpolation of our first-principles EOS data, linear mixing of pure C and pure H overestimates the volume of CH by 2.3% at 3.2 ×104 K (378 GPa). The volume difference ΔVmix decreases to within ±0.5% at T>5.6×104 K, which is consistent with the threshold temperature above which we see disappearance of peaks in the nuclear-pair correlation function g(r) plots (Fig. 9). On the other hand, the energy of the linear mixture is higher than the first-principles value by 1.1 eV/atom at 3.2 ×104 K. The value of ΔEmix decreases to 0-0.2 eV/atom at 7.5 ×104 K and remains less than 2.5 eV/atom at higher temperatures. The fact that the energy of the linear mixture is smaller at the highest temperatures, while pressure is similar, explains why linear mixing predicts CH to be stiffer at the compression maximum than our direct first-principles predictions (Fig. 3 in Ref. 28).

FIG. 10.

The difference in energy (top) and volume (bottom) between bi-variable spline fitting of our first-principles EOS data of CH and the linear mixing approximation at a series of T, P conditions along the Hugoniot curve. The symbols are defined as ΔEMix(x)=E(x)(1x)ECxEH and ΔVMix(x)=V(x)(1x)VCxVH.

FIG. 10.

The difference in energy (top) and volume (bottom) between bi-variable spline fitting of our first-principles EOS data of CH and the linear mixing approximation at a series of T, P conditions along the Hugoniot curve. The symbols are defined as ΔEMix(x)=E(x)(1x)ECxEH and ΔVMix(x)=V(x)(1x)VCxVH.

Close modal

At lower pressure and temperature, clear signatures of chemical bonds exist. Figure 9 compares the g(r) profile of C2H and CH2 at two different densities (2 ×ρ0 and 3 ×ρ0, with ρ0 = 1.76 and 2.24 g/cm3 for C2H and CH2, respectively) and three different temperatures. For C–C g(r) functions in Figs. 9(c) and 9(f), the results show clear peaks and structures at both temperatures of 6.7 ×103 and 104 K. These indicate the formation of carbon clusters. C–H bonds [Figs. 9(b) and 9(e)] also exist at these temperatures, characterized by the peak in g(r) at r 1.15 Å. The C–H bonds are not very stable and are significantly weakened by thermal and compressional effects, as the differences in peak height show. We do not see any evidence of stable H–H bonds even at the lowest temperature (6.7×103 K) that we considered [Figs. 9(a) and 9(d)], which is consistent with the analysis of other high pressure hydrogen-rich materials.128,129 Interestingly, Ref. 130 reported the formation of H2 molecules during the dissociation of silane at low densities and temperatures of 1000-4000 K. Considerable amount of H2 molecules was also found in H2–H2O mixtures at pressures below 35 GPa and a temperature of 2000 K.129 Those conditions are much colder than we have considered in this study. In our simulations, the lifetime of H–H bonds is generally very short (<5 fs, see the following discussion and Table I), which indicates that no stable H2 molecule can be formed. The peaks in g(r) do not seem to be strongly dependent on the overall C:H ratio in the system and are consistent with findings in recent DFT-MD simulations131 of CH4.

A snapshot from the DFT-MD simulations of CH and its electronic density distribution at 3.15 g/cm3 and 2×104 K is shown in Fig. 11. The disorder in the atomic positions is indicative of the plasma behavior, wherein ions participate in short-lived chemical bonding. A detailed structural analysis of the atomic bonding and lifetime132 at this condition (see Table I) indicates that C–C clusters and chains exist stably for the time scale of 10-100 fs, approximately one order of magnitude longer than H–H bonds. The lifetime of C–H bonds is slightly longer than 10 fs at T < 104 K and ρ < 3.15 g/cm3, and it is shorter than 10 fs and about twice the value of H–H bonds at T > 104 K.

FIG. 11.

A snapshot of the electron density profile of CH in the polymeric state131 around thermodynamic equilibrium at 3.15 g/cm3 and 2 ×104 K. The C and H atoms are represented by blue and pink spheres, respectively. Electron density which mitigates the bonding effects is illustrated by the light brown isosurface.

FIG. 11.

A snapshot of the electron density profile of CH in the polymeric state131 around thermodynamic equilibrium at 3.15 g/cm3 and 2 ×104 K. The C and H atoms are represented by blue and pink spheres, respectively. Electron density which mitigates the bonding effects is illustrated by the light brown isosurface.

Close modal

Changes in chemical bonding in hydrocarbons have been proposed to interpret experimental results along the Hugoniot curves. For example, Barrios et al.75 tentatively attributed a slight softening that is observed for polystyrene at 2-4 Mbar to the decomposition of chemical bonds. Bond dissociation is also included in the SESAME EOS model for CH at 2-4 Mbar.3 Our findings of dramatic decrease in the lifetimes of C–H bonds and changes in g(r) at 0.4-4 Mbar are consistent with the previous calculations.81,83,93,96 However, our analysis shows that the decrease in bond lifetime is gradual, spanning a few Mbar and tens of thousands of Kelvin. This indicates that the dissociation of chemical bonds is continuous along the Hugoniot curve, instead of suddenly complete at certain T and P.

We also investigated the electronic density of states of CH at the same (ρ, T) conditions as in the g(r) analyses (Fig. 9). We find that a pseudo-bandgap exists at the valence band maximum at 6.7 ×103 K. This gap is partially filled, resulting in a more continuous transition between the valence and the conduction bands, at 104 K, and is completely filled at 2 ×104 K (see Fig. 12). Closure of the gap indicates metalization of the system, which increases the electrical conductivity and reflectivity that can be observed in experiments. The corresponding pressure range (1.5-2.5 Mbar) is in accord with experimental findings71,75 of optical reflectivity changes of CH samples. Note that bandgap closure does not necessarily accompany complete chemical decomposition because the changes in the lifetimes of the chemical bonds are gradual. It is therefore not appropriate to equate the origin of reflectivity change with chemical bond dissociation. We also do not see the changes in bonding to have an obvious effect on the shape of the Hugoniot curve.

FIG. 12.

Electronic density of states (DOS) of CH at a series of temperatures and 3.15 g/cm3 based on DFT simulations. The dashed curves denote the occupied states. The Fermi energies are aligned at E = 0 eV. A pseudo-bandgap exists at 6.7 ×103 K. With increasing temperature, the gap is gradually filled. We have compared the results by using the Γ point, and 2 × 2 × 2 and 4 × 4 × 4 Monkhorst-Pack k meshes,133 and found very similar relationships between the three DOS curves.

FIG. 12.

Electronic density of states (DOS) of CH at a series of temperatures and 3.15 g/cm3 based on DFT simulations. The dashed curves denote the occupied states. The Fermi energies are aligned at E = 0 eV. A pseudo-bandgap exists at 6.7 ×103 K. With increasing temperature, the gap is gradually filled. We have compared the results by using the Γ point, and 2 × 2 × 2 and 4 × 4 × 4 Monkhorst-Pack k meshes,133 and found very similar relationships between the three DOS curves.

Close modal

ICF experiments routinely use GDP as an ablator material; GDP is mostly hydrocarbon (CH1.36) doped with small amounts of heavier elements, such as O or Ge. As has been shown in Sec. IV A and in Ref. 28, the linear mixing approximation is a reasonable way of estimating the EOS and shock compression of hydrocarbons. In this section, we apply this approximation to study the shock compression of GDP, compared with other EOS models, and investigate the effects of varying hydrogen and oxygen concentrations on the shock Hugoniot curve.

Figure 6 shows the Hugoniot curve of GDP (C43H56O), in comparison with those given by other models and that of CH. The initial density of GDP is set to 1.05 g/cm3, as relevant to polystyrene and to the hydrocarbon materials used in recent laser shock experiments.79 The initial energy E0 is determined by approximating GDP as an ideal mixture of three polymers—PAMS (C9H10), polyethylene (C2H4), and polyvinyl alcohol (C2H4O)—which allows for a higher flexibility in the composition of GDP. The energy of each polymer is determined as described in the supplementary material of Ref. 28. We determine the energy of diamond, isolated H2, and O2 molecules using DFT calculations and combine them with tabulated thermochemical data on the enthalpy of combustion134,135 to estimate the energy of these polymers. As a proof of concept, we compare the energy of coronene C24H12 determined as a combination of PAMS and polyethylene to the thermochemical data and find very small difference (less than 15 mHa/carbon). Initial densities in Fig. 13 are determined in the ideal mixture approximation, using the density of PAMS (1.075 g/cm3), polyethylene (0.95 g/cm3), and polyvinyl alcohol (1.19 g/cm3).

FIG. 13.

Comparison of the effects of varying oxygen (a) and hydrogen (b) contents on the shock Hugoniot curve of GDP.

FIG. 13.

Comparison of the effects of varying oxygen (a) and hydrogen (b) contents on the shock Hugoniot curve of GDP.

Close modal

Figure 6 shows the Hugoniot curve of GDP obtained from our first-principles EOS. It is slightly stiffer than that of CH at low pressures (P < 5 Mbar) and near the compression maximum. Similar trends are found in the results of the QSEM model.98 For compression ratios between 3.3 and 4.3, a shoulder develops along the Hugoniot curve. The shoulder structure is also found along the Hugoniot curve of LEOS 5400 GDP, which exhibits higher compressibility than that predicted by the first-principles calculations in this work. This may be traced to the start of ionization of the 1s electron shell of carbon, which leads to the shoulder, and the much lower Grüneisen γ used in LEOS 5400 than in LEOS 5112 for compression ratios larger than 3.5, which causes the softer behavior predicted by LEOS 5400.

Considering that the chemical composition of GDP ablators varies,79,83,98,119 it is thus useful to compare the Hugoniot curves of GDP with different C:H ratios and oxygen contents. We consider three oxygen mass percentages of 2.7%, 12.2%, and 21.8% and C:H ratios of 1:1, 1:1.33, and 1:1.5. A comparison of the Hugoniot curves is shown in Fig. 13. With the addition of oxygen, the pressure at the maximum compression increases, while the compression ratio does not show any observable change. The effect of oxygen can be understood from the fact that its 1s electrons are more strongly bound to the oxygen nuclei, which requires a higher temperature for ionization. Changes to the maximum compression ratio are insignificant within the range of oxygen content that we consider because the carbon 1s electrons dominate over those of oxygen. Increasing the amount of hydrogen leads to a decrease by 0.1 in the compression maximum when the C:H ratio decreases from 1:1 to 1:1.5, as shown in Fig. 13(b). This is the same trend with composition that has been seen in Fig. 3 of Ref. 28. The pressure at the compression maximum is not affected.

In this work, we presented the results of PIMC and DFT-MD simulations of a series of hydrocarbons. We obtained accurate internal energies and pressures from temperatures of 6.7 ×103 K–1.3 ×108 K. PIMC and DFT-MD were shown to be consistent at 106 K, typically to within 1 Ha/carbon in energy and 3% in pressure. This cross-validates the use of both methods at the temperature of 106 K. We used these results to evaluate some of the detailed assumptions made in a recent EOS model for CH, LEOS-5112.

We investigated the principal Hugoniot curves using the obtained EOS data and found a maximum compression of 4.7, which is similar to that predicted by LEOS 5112 and 5400 but larger than SESAME 7593 and OF-DFT predictions. We expect that future experiments will test this prediction.

We demonstrated the validity of the linear mixing approximation in obtaining the EOS and shock Hugoniot curves of hydrocarbons. This can be explained by the unstable, short-lived C–H chemical bonds (lifetime τ < 4.4 fs) for temperatures greater than 6.7×104 K. The nuclear-pair correlation function g(r) of hydrocarbons resembles that of a simple atomic liquid at the higher temperatures we considered. At lower temperatures, g(r) as well as the bond lifetime analysis of hydrocarbon systems shows the possible existence of stable C–C bonds with lifetimes of 10-100 fs, weak C–H bonds with lifetimes of 4-16 fs, and no signature of stable H–H bonds.

By applying the linear mixing approximation, we investigated the Hugoniot curves of GDP as a function of oxygen content and C:H ratios. We found that the compression maximum remains unchanged when varying the oxygen mass percentage between 0% and 21.8% while the pressure increases by about 0.1 Gbar. When the C:H ratio decreases from 1:1 to 1:1.5, the shock compression maximum decreases by 0.1, while the pressure, which is determined by the 1s ionization of carbon, does not change.

Our results provide a benchmark for future theoretical investigations of the EOS of hydrocarbons and should be useful for informing on-going dynamic compression experiments aimed at reaching Gbar conditions.

See the supplementary material for the tables of EOS data of C2H, CH, C2H3, CH2, CH3, and CH4 considered in this study.

This research is supported by the U. S. Department of Energy, Grant Nos. DE-SC0010517, DE-SC0016248, and DE-NA0001859. Computational support was provided by the Blue Waters sustained-petascale computing Project (No. NSF ACI 1640776), which is supported by the National Science Foundation (Award Nos. OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. S.Z. is partially supported by the PLS-Postdoctoral Grant of the Lawrence Livermore National Laboratory. S.Z. and B.M. thank Benjamin D. Hammel for putting forward the interesting question about bond dissociation. S.Z. appreciates Miguel Morales for helpful discussions. This work was in part performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344.

1.
W.-H.
Tang
,
B.-B.
Xu
,
X.-W.
Ran
, and
Z.-H.
Xu
,
Acta Phys. Sin.
66
,
030505
(
2017
).
2.
R. M.
More
,
K. H.
Warren
,
D. A.
Young
, and
G. B.
Zimmerman
,
Phys. Fluids
31
,
3059
(
1988
).
3.
SESAME: The Los Alamos National Laboratory Equation of State Database
, Report No. LA-UR-92–3407, edited by
S. P.
Lyon
and
J. D.
Johnson
, Group T-1,
1992
.
4.
H. F.
Wilson
and
B.
Militzer
,
Phys. Rev. Lett.
104
,
121101
(
2010
).
5.
H. F.
Wilson
and
B.
Militzer
,
Phys. Rev. Lett.
108
,
111101
(
2012
).
6.
H. F.
Wilson
,
M. L.
Wong
, and
B.
Militzer
,
Phys. Rev. Lett.
110
,
151102
(
2013
).
7.
S. M.
Wahl
and
B.
Militzer
,
Earth Planet. Sci. Lett.
410
,
25
(
2015
).
8.
S.
Zhang
,
S.
Cottaar
,
T.
Liu
,
S.
Stackhouse
, and
B.
Militzer
,
Earth Planet. Sci. Lett.
434
,
264
(
2016
).
9.
F.
Soubiran
,
B.
Militzer
,
K. P.
Driver
, and
S.
Zhang
,
Phys. Plasmas
24
,
041401
(
2017
).
10.
M. A.
Morales
,
J. M.
McMahon
,
C.
Pierleoni
, and
D. M.
Ceperley
,
Phys. Rev. Lett.
110
,
065702
(
2013
).
11.
M. A.
Morales
,
J. M.
McMahon
,
C.
Pierleoni
, and
D. M.
Ceperley
,
Phys. Rev. B
87
,
184107
(
2013
).
12.
D. M.
Ceperley
,
Rev. Mod. Phys.
67
,
279
(
1995
).
13.
D. M.
Ceperley
, in
Monte Carlo and Molecular Dynamics of Condensed Matter Systems
, edited by
K.
Binder
and
G.
Ciccotti
(
Editrice Compositori
,
Bologna, Italy
,
1996
), Vol. 49, p.
443
.
14.
C.
Pierleoni
,
D. M.
Ceperley
,
B.
Bernu
, and
W. R.
Magro
,
Phys. Rev. Lett.
73
,
2145
(
1994
).
15.
B.
Militzer
and
D. M.
Ceperley
,
Phys. Rev. E
63
,
066404
(
2001
).
16.
B.
Militzer
,
Phys. Rev. B
79
,
155105
(
2009
).
17.
K. P.
Driver
and
B.
Militzer
,
Phys. Rev. Lett.
108
,
115502
(
2012
).
18.
B.
Militzer
and
K. P.
Driver
,
Phys. Rev. Lett.
115
,
176403
(
2015
).
19.
L. X.
Benedict
,
K. P.
Driver
,
S.
Hamel
,
B.
Militzer
,
T.
Qi
,
A. A.
Correa
,
A.
Saul
, and
E.
Schwegler
,
Phys. Rev. B
89
,
224109
(
2014
).
20.
K. P.
Driver
and
B.
Militzer
,
Phys. Rev. B
91
,
045103
(
2015
).
21.
K. P.
Driver
,
F.
Soubiran
,
S.
Zhang
, and
B.
Militzer
,
J. Chem. Phys.
143
,
164507
(
2015
).
22.
K. P.
Driver
and
B.
Militzer
,
Phys. Rev. B
93
,
064101
(
2016
).
23.
S. X.
Hu
,
B.
Militzer
,
L. A.
Collins
,
K. P.
Driver
, and
J. D.
Kress
,
Phys. Rev. B
94
,
094109
(
2016
).
24.
S.
Zhang
,
K. P.
Driver
,
F.
Soubiran
, and
B.
Militzer
,
High Energy Density Phys.
21
,
16
(
2016
).
25.
K. P.
Driver
,
F.
Soubiran
,
S.
Zhang
, and
B.
Militzer
,
High Energy Density Phys.
23
,
81
(
2017
).
26.
K. P.
Driver
and
B.
Militzer
,
Phys. Rev. E
95
,
043205
(
2017
).
27.
S.
Zhang
,
K. P.
Driver
,
F.
Soubiran
, and
B.
Militzer
,
J. Chem. Phys.
146
,
074505
(
2017
).
28.
S.
Zhang
,
K. P.
Driver
,
F.
Soubiran
, and
B.
Militzer
,
Phys. Rev. E
96
,
013204
(
2017
).
29.
C.
Cazorla
and
J.
Boronat
,
Rev. Mod. Phys.
89
,
035003
(
2017
).
30.
A. K.
Rappé
,
C. J.
Casewit
,
K.
Colwell
,
W.
Goddard Iii
, and
W.
Skiff
,
J. Am. Chem. Soc.
114
,
10024
(
1992
).
31.
X.
Gonze
and
C.
Lee
,
Phys. Rev. B
55
,
10355
(
1997
).
32.
S.
Baroni
,
S.
De Gironcoli
,
A.
Dal Corso
, and
P.
Giannozzi
,
Rev. Mod. Phys.
73
,
515
(
2001
).
33.
W. M.
Foulkes
,
L.
Mitas
,
R. J.
Needs
, and
G.
Rajagopal
,
Rev. Mod. Phys.
73
,
33
(
2001
).
34.
F.
Zhou
,
W.
Nielson
,
Y.
Xia
,
V.
Ozoliņš
 et al,
Phys. Rev. Lett.
113
,
185501
(
2014
).
35.
R. P.
Feynman
,
Statistical Mechanics
(
Addison-Wesley
,
Reading, MS
,
1972
).
36.
M.
Jones
and
D.
Ceperley
,
Phys. Rev. Lett.
76
,
4572
(
1996
).
37.
B.
Militzer
and
R. L.
Graham
,
J. Phys. Chem. Solids
67
,
2136
(
2006
).
38.
K.-d.
Oh
and
P.
Deymier
,
Phy. Rev. B
58
,
7577
(
1998
).
39.
C.
Pierleoni
and
D.
Ceperley
, “
The coupled electron-ion Monte Carlo method
,” in
Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology
, edited by
M.
Ferrario
,
G.
Ciccotti
, and
K.
Binder
(
Springer Berlin Heidelberg
,
Berlin, Heidelberg
,
2006
), Vol. 1, pp.
641
683
.
40.
F.
Graziani
,
M. P.
Desjarlais
,
R.
Redmer
, and
S. B.
Trickey
,
Frontiers and Challenges in Warm Dense Matter
(
Springer Science & Business
,
2014
), Vol. 96.
41.
D.
Kang
,
H.
Sun
,
J.
Dai
,
W.
Chen
,
Z.
Zhao
,
Y.
Hou
,
J.
Zeng
, and
J.
Yuan
,
Sci. Rep.
4
,
5484
(
2014
).
42.
J. M.
McMahon
,
M. A.
Morales
,
C.
Pierleoni
, and
D. M.
Ceperley
,
Rev. Mod. Phys.
84
,
1607
(
2012
).
43.
M. A.
Morales
,
C.
Pierleoni
,
E.
Schwegler
, and
D.
Ceperley
,
Proc. Natl. Acad. Sci. U. S. A.
107
,
12799
(
2010
).
44.
C.
Pierleoni
,
M. A.
Morales
,
G.
Rillo
,
M.
Holzmann
, and
D. M.
Ceperley
,
Proc. Natl. Acad. Sci. U. S. A.
113
,
4953
(
2016
).
45.
E.
Pollock
and
D.
Ceperley
,
Phys. Rev. B
30
,
2555
(
1984
).
46.
B.
Chen
,
I.
Ivanov
,
M. L.
Klein
, and
M.
Parrinello
,
Phys. Rev. Lett.
91
,
215503
(
2003
).
47.
J. A.
Morrone
and
R.
Car
,
Phys. Rev. Lett.
101
,
017801
(
2008
).
48.
X.-Z.
Li
,
B.
Walker
, and
A.
Michaelides
,
Proc. Natl. Acad. Sci. U. S. A.
108
,
6369
(
2011
).
49.
M.
Ceriotti
,
W.
Fang
,
P. G.
Kusalik
,
R. H.
McKenzie
,
A.
Michaelides
,
M. A.
Morales
, and
T. E.
Markland
,
Chem. Rev.
116
,
7529
(
2016
).
50.
X.-Z.
Li
,
M. I. J.
Probert
,
A.
Alavi
, and
A.
Michaelides
,
Phys. Rev. Lett.
104
,
066102
(
2010
).
51.
M.
Benoit
,
D.
Marx
, and
M.
Parrinello
,
Comput. Mater. Sci.
10
,
88
(
1998
).
52.
B.
Pamuk
,
J. M.
Soler
,
R.
Ramírez
,
C.
Herrero
,
P.
Stephens
,
P.
Allen
, and
M.-V.
Fernández-Serra
,
Phys. Rev. Lett.
108
,
193003
(
2012
).
53.
R.
Ramírez
,
C. P.
Herrero
, and
E. R.
Hernández
,
Phys. Rev. B
73
,
245202
(
2006
).
54.
R. O.
Weht
,
J.
Kohanoff
,
D. A.
Estrin
, and
C.
Chakravarty
,
J. Chem. Phys.
108
,
8848
(
1998
).
55.
A. W.
Hauser
,
A. O.
Mitrushchenkov
, and
M. P.
de Lara-Castells
,
J. Phys. Chem. C
121
,
3807
(
2017
).
56.
R.
Betti
and
O.
Hurricane
,
Nat. Phys.
12
,
435
(
2016
).
57.
N.
Meezan
,
M.
Edwards
,
O.
Hurricane
,
P.
Patel
,
D.
Callahan
,
W.
Hsing
,
R.
Town
,
F.
Albert
,
P.
Amendt
,
L. B.
Hopkins
 et al,
Plasma Phys. Controlled Fusion
59
,
014021
(
2016
).
58.
V.
Goncharov
,
S.
Regan
,
E.
Campbell
,
T.
Sangster
,
P.
Radha
,
J.
Myatt
,
D.
Froula
,
R.
Betti
,
T.
Boehly
,
J.
Delettrez
 et al,
Plasma Phys. Controlled Fusion
59
,
014008
(
2016
).
59.
T.
Guillot
,
Annu. Rev. Earth Planet. Sci.
33
,
493
(
2005
).
60.
G.
Wallerstein
,
I.
Iben
,
P.
Parker
,
A. M.
Boesgaard
,
G. M.
Hale
,
A. E.
Champagne
,
C. A.
Barnes
,
F.
Käppeler
,
V. V.
Smith
,
R. D.
Hoffman
 et al,
Rev. Mod. Phys.
69
,
995
(
1997
).
61.
G.
Hauver
and
A.
Melani
, “Shock compression of plexiglas and polystyrene,” Technical Report, DTIC Document, 1964.
62.
I.
Dudoladov
,
V.
Rakitin
,
Y. N.
Sutulov
, and
G.
Telegin
,
J. Appl. Mech. Tech. Phys.
10
,
673
(
1969
).
63.
D. L.
Lamberson
,
J. R.
Asay
, and
A. H.
Guenther
,
J. Appl. Phys.
43
,
976
(
1972
).
64.
S. P.
Marsh
,
LASL Shock Hugoniot Data
(
University of California Press
,
1980
), Vol. 5.
65.
W. J.
Nellis
,
F. H.
Ree
,
R. J.
Trainor
,
A. C.
Mitchell
, and
M. B.
Boslough
,
J. Chem. Phys.
80
,
2789
(
1984
).
66.
R.
Kodama
,
K.
Tanaka
,
M.
Nakai
,
K.
Nishihara
,
T.
Norimatsu
,
T.
Yamanaka
, and
S.
Nakai
,
Phys. Fluids B
3
,
735
(
1991
).
67.
A.
Bushman
,
I.
Lomonosov
,
V.
Fortov
,
K.
Khishchenko
,
M.
Zhernokletov
, and
Y. N.
Sutulov
,
Zh. Eksp. Teor. Fiz.
109
,
1662
1670
(
1996
)
A.
Bushman
,
I.
Lomonosov
,
V.
Fortov
,
K.
Khishchenko
,
M.
Zhernokletov
, and
Y. N.
Sutulov
, [
J. Exp. Theor. Phys.
82
,
895
(
1996
)], available at http://jetp.ac.ru/cgi-bin/dn/e_082_05_0895.pdf.
68.
M.
Koenig
,
A.
Benuzzi
,
B.
Faral
,
J.
Krishnan
,
J.
Boudenne
,
T.
Jalinaud
,
C.
Rémond
,
A.
Decoster
,
D.
Batani
,
D.
Beretta
 et al,
Appl. Phys. Lett.
72
,
1033
(
1998
).
69.
R.
Cauble
,
L. B. D.
Silva
,
T. S.
Perry
,
D. R.
Bach
,
K. S.
Budil
,
P.
Celliers
,
G. W.
Collins
,
A.
Ng
,
J. T. W.
Barbee
,
B. A.
Hammel
,
N. C.
Holmes
,
J. D.
Kilkenny
,
R. J.
Wallace
,
G.
Chiu
, and
N. C.
Woolsey
,
Phys. Plasmas
4
,
1857
(
1997
).
70.
R.
Cauble
,
T.
Perry
,
D.
Bach
,
K.
Budil
,
B.
Hammel
,
G.
Collins
,
D.
Gold
,
J.
Dunn
,
P.
Celliers
,
L.
Da Silva
 et al,
Phys. Rev. Lett.
80
,
1248
(
1998
).
71.
M.
Koenig
,
F.
Philippe
,
A.
Benuzzi-Mounaix
,
D.
Batani
,
M.
Tomasini
,
E.
Henry
, and
T.
Hall
,
Phys. Plasmas
10
,
3026
(
2003
).
72.
N.
Ozaki
,
T.
Ono
,
K.
Takamatsu
,
K.
Tanaka
,
M.
Nakano
,
T.
Kataoka
,
M.
Yoshida
,
K.
Wakabayashi
,
M.
Nakai
,
K.
Nagai
 et al,
Phys. Plasmas
12
,
124503
(
2005
).
73.
S.
Hu
,
V.
Smalyuk
,
V.
Goncharov
,
J.
Knauer
,
P.
Radha
,
I.
Igumenshchev
,
J.
Marozas
,
C.
Stoeckl
,
B.
Yaakobi
,
D.
Shvarts
 et al,
Phys. Rev. Lett.
100
,
185003
(
2008
).
74.
N.
Ozaki
,
T.
Sano
,
M.
Ikoma
,
K.
Shigemori
,
T.
Kimura
,
K.
Miyanishi
,
T.
Vinci
,
F.
Ree
,
H.
Azechi
,
T.
Endo
 et al,
Phys. Plasmas
16
,
062702
(
2009
).
75.
M.
Barrios
,
D.
Hicks
,
T.
Boehly
,
D.
Fratanduono
,
J.
Eggert
,
P.
Celliers
,
G.
Collins
, and
D.
Meyerhofer
,
Phys. Plasmas
17
,
056307
(
2010
).
76.
H.
Shu
,
S.
Fu
,
X.
Huang
,
G.
Jia
,
H.
Zhou
,
J.
Wu
,
J.
Ye
, and
Y.
Gu
,
Chin. Opt. Lett.
8
,
1142
(
2010
).
77.
W.
Shang
,
H.
Wei
,
Z.
Li
,
R.
Yi
,
T.
Zhu
,
T.
Song
,
C.
Huang
, and
J.
Yang
,
Phys. Plasmas
20
,
102702
(
2013
).
78.
H.
Shu
,
X.
Huang
,
J.
Ye
,
J.
Wu
,
G.
Jia
,
Z.
Fang
,
Z.
Xie
,
H.
Zhou
, and
S.
Fu
,
Eur. Phys. J. D
69
,
259
(
2015
).
79.
M.
Barrios
,
T.
Boehly
,
D.
Hicks
,
D.
Fratanduono
,
J.
Eggert
,
G.
Collins
, and
D.
Meyerhofer
,
J. Appl. Phys.
111
,
093515
(
2012
).
80.
G.
Huser
,
N.
Ozaki
,
T.
Sano
,
Y.
Sakawa
,
K.
Miyanishi
,
G.
Salin
,
Y.
Asaumi
,
M.
Kita
,
Y.
Kondo
,
K.
Nakatsuka
 et al,
Phys. Plasmas
20
,
122703
(
2013
).
81.
G.
Huser
,
V.
Recoules
,
N.
Ozaki
,
T.
Sano
,
Y.
Sakawa
,
G.
Salin
,
B.
Albertazzi
,
K.
Miyanishi
, and
R.
Kodama
,
Phys. Rev. E
92
,
063108
(
2015
).
82.
A. S.
Moore
,
S.
Prisbrey
,
K. L.
Baker
,
P. M.
Celliers
,
J.
Fry
,
T. R.
Dittrich
,
K.-J. J.
Wu
,
M. L.
Kervin
,
M. E.
Schoff
,
M.
Farrell
 et al,
High Energy Density Phys.
20
,
23
(
2016
).
83.
S.
Hamel
,
L. X.
Benedict
,
P. M.
Celliers
,
M.
Barrios
,
T.
Boehly
,
G.
Collins
,
T.
Döppner
,
J.
Eggert
,
D.
Farley
,
D.
Hicks
 et al,
Phys. Rev. B
86
,
094113
(
2012
).
84.
D.
Kraus
,
D. A.
Chapman
,
A. L.
Kritcher
,
R. A.
Baggott
,
B.
Bachmann
,
G. W.
Collins
,
S. H.
Glenzer
,
J. A.
Hawreliak
,
D. H.
Kalantar
,
O. L.
Landen
,
T.
Ma
,
S.
Le Pape
,
J.
Nilsen
,
D. C.
Swift
,
P.
Neumayer
,
R. W.
Falcone
,
D. O.
Gericke
, and
T.
Döppner
,
Phys. Rev. E
94
,
011202
(
2016
).
85.
D. C.
Swift
,
J. A.
Hawreliak
,
D.
Braun
,
A.
Kritcher
,
S.
Glenzer
,
G.
Collins
,
S. D.
Rothman
,
D.
Chapman
, and
S.
Rose
,
AIP Conf. Proc.
1426
,
477
(
2012
).
86.
T.
Döppner
,
A.
Kritcher
,
D.
Kraus
,
S.
Glenzer
,
B.
Bachmann
,
D.
Chapman
,
G.
Collins
,
R.
Falcone
,
J.
Hawreliak
,
O.
Landen
 et al,
J. Phys.: Conf. Ser.
500
,
192019
(
2014
).
87.
A.
Kritcher
,
T.
Döppner
,
D.
Swift
,
J.
Hawreliak
,
G.
Collins
,
J.
Nilsen
,
B.
Bachmann
,
E.
Dewald
,
D.
Strozzi
,
S.
Felker
 et al,
High Energy Density Phys.
10
,
27
(
2014
).
88.
A.
Kritcher
,
T.
Doeppner
,
D.
Swift
,
J.
Hawreliak
,
J.
Nilsen
,
J.
Hammer
,
B.
Bachmann
,
G.
Collins
,
O.
Landen
,
C.
Keane
 et al,
J. Phys.: Conf. Ser.
688
,
012055
(
2016
).
89.
J.
Nilsen
,
B.
Bachmann
,
G.
Zimmerman
,
R.
Hatarik
,
T.
Döppner
,
D.
Swift
,
J.
Hawreliak
,
G.
Collins
,
R.
Falcone
,
S.
Glenzer
 et al,
High Energy Density Phys.
21
,
20
(
2016
).
90.
R.
Nora
,
W.
Theobald
,
R.
Betti
,
F.
Marshall
,
D.
Michel
,
W.
Seka
,
B.
Yaakobi
,
M.
Lafon
,
C.
Stoeckl
,
J.
Delettrez
 et al,
Phys. Rev. Lett.
114
,
045001
(
2015
).
91.
T.
Doeppner
and
A.
Kritcher
, private communication (
2016
).
92.
T. R.
Mattsson
,
J. M. D.
Lane
,
K. R.
Cochrane
,
M. P.
Desjarlais
,
A. P.
Thompson
,
F.
Pierce
, and
G. S.
Grest
,
Phys. Rev. B
81
,
054103
(
2010
).
93.
C.
Wang
,
X.-T.
He
, and
P.
Zhang
,
Phys. Plasmas
18
,
082707
(
2011
).
94.
F.
Lambert
and
V.
Recoules
,
Phys. Rev. E
86
,
026405
(
2012
).
95.
S.
Hu
,
T.
Boehly
,
L.
Collins
 et al,
Phys. Rev. E
89
,
063104
(
2014
).
96.
S.
Hu
,
L.
Collins
,
V.
Goncharov
,
J.
Kress
,
R.
McCrory
,
S.
Skupsky
 et al,
Phys. Rev. E
92
,
043104
(
2015
).
97.
S.
Hu
,
L.
Collins
,
V.
Goncharov
,
J.
Kress
,
R.
McCrory
, and
S.
Skupsky
,
Phys. Plasmas
23
,
042704
(
2016
).
98.
P.
Colin-Lalu
,
V.
Recoules
,
G.
Salin
,
T.
Plisson
,
E.
Brambrink
,
T.
Vinci
,
R.
Bolis
, and
G.
Huser
,
Phys. Rev. E
94
,
023204
(
2016
).
99.
B.
Militzer
, Ph.D. thesis,
University of Illinois at Urbana-Champaign
,
2000
.
100.
D. M.
Ceperley
,
J. Stat. Phys.
63
,
1237
(
1991
).
101.
W. R.
Magro
,
D. M.
Ceperley
,
C.
Pierleoni
, and
B.
Bernu
,
Phys. Rev. Lett.
76
,
1240
(
1996
).
102.
B.
Militzer
,
D. M.
Ceperley
,
J. D.
Kress
,
J. D.
Johnson
,
L. A.
Collins
, and
S.
Mazevet
,
Phys. Rev. Lett.
87
,
275502
(
2001
).
103.
S. X.
Hu
,
B.
Militzer
,
V. N.
Goncharov
, and
S.
Skupsky
,
Phys. Rev. Lett.
104
,
235003
(
2010
).
104.
B.
Militzer
and
D. M.
Ceperley
,
Phys. Rev. Lett.
85
,
1890
(
2000
).
105.
S. X.
Hu
,
B.
Militzer
,
V. N.
Goncharov
, and
S.
Skupsky
,
Phys. Rev. B
84
,
224109
(
2011
).
106.
B.
Militzer
,
W.
Magro
, and
D.
Ceperley
,
Contrib. Plasma Phys.
39
,
151
(
1999
).
107.
B.
Militzer
,
Phys. Rev. Lett.
97
,
175501
(
2006
).
108.
B.
Militzer
,
J. Low Temp. Phys.
139
,
739
(
2005
).
109.
V.
Natoli
and
D. M.
Ceperley
,
J. Comput. Phys.
117
,
171
(
1995
).
110.
B.
Militzer
,
Comput. Phys. Commun.
204
,
88
(
2016
).
111.
G.
Kresse
and
J.
Furthmüller
,
Phys. Rev. B
54
,
11169
(
1996
).
112.
P. E.
Blöchl
,
O.
Jepsen
, and
O. K.
Andersen
,
Phys. Rev. B
49
,
16223
(
1994
).
113.
D. M.
Ceperley
and
B. J.
Alder
,
Phys. Rev. Lett.
45
,
566
(
1980
).
114.
J. P.
Perdew
and
A.
Zunger
,
Phys. Rev. B
23
,
5048
(
1981
).
115.
S.
Nosé
,
J. Chem. Phys.
81
,
511
(
1984
).
116.
See http://opium.sourceforge.net for information about the OPIUM code.
117.
A.
Kramida
,
Yu.
Ralchenko
,
J.
Reader
, and
NIST ASD Team
, NIST Atomic Spectra Database, version 5.3, http://physics.nist.gov/asd, 3 August 2017, National Institute of Standards and Technology, Gaithersburg, MD, 2015.
118.
J. P.
Perdew
,
K.
Burke
, and
M.
Ernzerhof
,
Phys. Rev. Lett.
77
,
3865
(
1996
).
119.
P. A.
Sterne
,
L. X.
Benedict
,
S.
Hamel
,
A. A.
Correa
,
J. L.
Milovich
,
M. M.
Marinak
,
P. M.
Celliers
, and
D. E.
Fratanduono
,
J. Phys.: Conf. Ser.
717
,
012082
(
2016
).
120.
D. A.
Young
and
E. M.
Corey
,
J. Appl. Phys.
78
,
3748
(
1995
).
121.
B.
Wilson
,
V.
Sonnad
,
P.
Sterne
, and
W.
Isaacs
,
J. Quant. Spectrosc. Radiat. Transfer
99
,
658
(
2006
).
122.
M. A.
Meyers
,
Dynamic Behavior of Materials
(
Wiley
,
New York
,
1994
).
123.
S.
Zhang
,
H.
Wang
,
W.
Kang
,
P.
Zhang
, and
X. T.
He
,
Phys. Plasmas
23
,
042707
(
2016
).
124.
S.
Zhang
, Peking University, private communication (
2017
).
125.
D.
Wallace
,
Statistical Physics of Crystals and Liquids
(
World Scientific
,
2002
), pp.
155
217
.
126.
L. M.
Amzel
,
M. C. M.
Cucarella
, and
L. N.
Becka
,
J. Phys. Chem.
75
,
1073
(
1971
).
127.
V.
Arp
,
J. M.
Persichetti
, and
G.-b.
Chen
,
J. Fluids Eng.
106
,
193
(
1984
).
128.
J.
Vorberger
,
I.
Tamblyn
,
B.
Militzer
, and
S. A.
Bonev
,
Phys. Rev. B
75
,
024206
(
2007
).
129.
F.
Soubiran
and
B.
Militzer
,
Astrophys. J.
806
,
228
(
2015
).
130.
H.
Sun
,
D.
Kang
,
J.
Dai
,
J.
Zeng
, and
J.
Yuan
,
Phys. Rev. E
89
,
022128
(
2014
).
131.
B. L.
Sherman
,
H. F.
Wilson
,
D.
Weeraratne
, and
B.
Militzer
,
Phys. Rev. B
86
,
224113
(
2012
).
132.

We chose cut-off distances of 1.90 Å, 1.55 Å, and 1.00 Å for C–C, C–H, and H–H, respectively. These define a pattern of bonds for every configuration along the MD trajectory. Then we analyze how long particular double and triple bonds exist to determine the lifetime.

133.
H. J.
Monkhorst
and
J. D.
Pack
,
Phys. Rev. B
13
,
5188
(
1976
).
134.
D. E.
Roberts
and
R. S.
Jessup
,
J. Res. Natl. Bur. Stand.
46
,
11
(
1951
).
135.
R. N.
Walters
,
S. M.
Hackett
, and
R. E.
Lyon
,
Fire Mater.
24
,
245
(
2000
).

Supplementary Material