Inertial confinement fusion (ICF) with the laserindirectdrive scheme has recently made a tremendous breakthrough recently after decades of intensive research effort. Taking this success to the next step, the ICF community is coming to a general consensus that laser directdrive (LDD) fusion might be the viable way for enabling inertial fusion energy (IFE) and highgain targets for other applications. Designing and understanding LDD fusion targets heavily rely on radiationhydrodynamic code simulations, in which chargedparticle transport plays an essential role in modeling lasertarget energy coupling and bootstrap heating of fusionproduced αparticles. To better simulate chargedparticle transport in LDD targets, over the past four decades the plasma physics community has advanced transport calculations from simple plasma physics models to sophisticated computations based on firstprinciples methods. In this review, we give an overview of the current status of chargedparticle transport modeling for LDD fusion, including what challenges we still face and the possible paths moving forward to advance transport modeling for ICF simulations. We hope this review will provide a summary of exciting challenges to stimulate young minds to enter the field, facilitate further progress in understanding warmdense matter physics, and ultimately bridge toward the success of reliable LDD fusion designs for IFE and other highgain ICF applications.
I. INTRODUCTION
Since the concept of laser fusion^{1} was introduced in 1972, it has been a longstanding challenge for the plasma physics community to demonstrate inertial confinement fusion (ICF) ignition. After decades of intensive investigation, a tremendous breakthrough, including the Lawson criterion for ignition exceeded^{2} and a target gain of $G>1$ reached,^{3} was recently achieved for laser fusion. These recent successes in ICF have been made by using the socalled laserindirectdrive (LID) scheme at the National Ignition Facility (NIF).^{4} In the LID fusion scheme,^{5–9} the highenergy (∼2 MJ) laser beams are injected into a gold “hohlraum” that converts powerful UV lasers to highintensity and blackbody thermal x rays to drive an ICF capsule sitting in the center of the hohlraum. The highintensity thermal x rays ablate the ICF capsule consisting of an empty core filled with deuteriumtritium (DT) gas, a layer of solid DT as the main fusion fuel, and a thick highdensity carbon (HDC) layer serving as the ablator.
In the LID scheme, the violent xray ablation launches shocks into the capsule, which must be precisely controlled to compress the DT fuel and implode the capsule. The highpressure ablation and spherical convergence ultimately accelerate the shell (DT plus the leftover HDC ablator) to a high implosion velocity ( $ v imp>370$ km/s), having a kinetic energy of $ E K\u226520$ kJ. At the end of implosion, the highspeed shell rapidly stagnates to create a hightemperature “hotspot” surrounded by a dense shell (due to return shock and convergence). Once the hotspot density and temperature are high enough to reach a pressure over ∼400 Gbar in a typical NIF target, the large number of αparticles produced by DT fusion can readily stop in the hotspot and give their kinetic energies to bootstrap heating the hotspot locally. Within a certain inertial confinement time provided by the stagnating dense shell, this bootstrapheating process further enhances the DT fusion production and a burn wave can then be initiated to propagate into the stagnated dense DT shell and to burnoff a certain fraction of the DT fuel, leading to ignition and, ultimately, fusion gain.
The successful demonstration of ignition and target gain of $G>1$ by the LID scheme has proven the fundamental physics (and engineering) principles of ICF. These successes^{2,3} are surely cornerstones in the history of pursuing controlled laser fusion by mankind. However, there are still plenty of challenges to reach the highgain ( $G\u226b1)$ “compressive burn” regime, for which high compression areal density ( $\rho R)$ is required to provide a longer confinement time to increase the burn fraction of DT fuel. Given the low wallplug efficiency of conventional solidstate lasers ( $\u22721%)$, a high target gain ( $G\u226550\u2013100$) is a necessary condition for commercializing inertial fusion energy (IFE) and for other important highyield applications. For example, to reach a neutronyield level of ∼100 MJ energy, one needs a gain of $G=40\u201350$ for laser drive energy of $\u223c2\u20132.5$ MJ. Due to the low lasertarget coupling efficiency of LID and due to the requirement of lasertoxrays conversion, designing a highgain ( $G\u226550$) LID target is not a trivial task as far as current knowledge stands. Alternatively, the laser directdrive (LDD) scheme^{10–14} poses the advantage of having higher lasertarget coupling efficiency, even though there are other obstacles for LDD fusion. Specifically, for LDD implosions, twice as much laser energy can be coupled to the kinetic energy of the imploding shell than that of the LID scheme. This holds even when accounting for reduced ablation pressure in LDD due to crossbeam energy transfer^{15} (CBET). The higher hydro efficiency and simple target geometry make the LDD scheme appealing to highgain laser fusion for IFE and highyield stockpile stewardship applications.
The LDD target implosion dynamics is very similar to LID. The only difference lies in the drive literally: For LDD, laser beams illuminate the target directly, while thermal x rays enclosed in the hohlraum give rise to the needed ablation pressure for LID. Because of the difference in drive, the lasertarget coupling is sensitive to different physical properties of plasma physics. Namely, the ablation pressure in LID implosions depends on the xray radiation temperature (i.e., xray intensity) and the opacity of the ablator, while for LDD implosions, the energy coupling from laser to imploding target mainly depends on the thermal conduction of electrons through the socalled “conductionzone” plasmas. Namely, electrons heated in coronal plasmas directly by highpower lasers will carry heat flux to traverse the conductionzone plasmas to reach the ablation front of an LDD target. Thus, the transport modeling of heatcarrying electrons is key to simulate and understand lasertarget energy coupling for LDD implosion experiments. The electron transport applies not only to the region from corona to ablation front in LDD but also to the inside of the hotspot where ablation of the DT shell is part of the hotspot formation process for both LID and LDD implosions. Finally, accurate modeling of αparticle transport in the hotspot and denseshell DT plasmas is also critical for both LID and LDD target designs, as it determines bootstrap heating for ignition and burn wave propagation in ICF.
In this review, we provide an overview of the current status of modeling chargedparticle transport (electrons and αparticles) in radiationhydrodynamic simulations of LDD targets. Challenges and opportunities for improving chargedparticle transport models are also discussed to give certain perspectives on their implications to reliable LDD target designs for future highgain IFE and other highyield applications. To summarize the current understanding gaps of chargedparticle transport for LDD simulations, we give a list of basic needs for improvements: (1) A reliable electron thermal conductivity model is needed in the warmdense matter regime, which determines heat transport between the ablator and DT layers; (2) An accurate electron meanfree path ( $ \lambda e)$ model is required to resolve the ablator/DT interface retreating discrepancy observed in LDD experiments, as $ \lambda e$ controls nonlocal electron energy transport through the conductionzone plasmas; and (3) a firstprinciples αparticle stopping power model is still needed for degenerated and dense DT shell conditions, which will give confidence for radhydro codes to reliably simulate burnwave propagation in highyield (highgain) ICF targets. These model improvements, benchmarked with focused experiments on OMEGA, will give radhydro codes the necessary confidence in both energetics and burn for future scaledup highyield and/or highgain target designs of LDD implosions.
The paper is organized as follows: In Sec. II, we first give the basics about a typical LDD target and laser pulse shapes used for implosions on both the Omega Laser Facility^{16} and NIF.^{4} We further discuss the typical conductionzone plasma conditions in which chargedparticle transport is concerned. We then give in Sec. III a summary of the traditional transport models of electron heat conduction used in radiationhydrodynamics codes for LDD simulations. In Sec. IV, the stateoftheart thermal conductivity models based on firstprinciple groundstate densityfunctional theory (DFT) calculations are reviewed for deuterium (and DT by mass scaling) and polystyrene (CH). Their effects on LDD implosion simulations are also demonstrated in this section by using the local heat conduction modeling. As the radhydro codes are moving to nonlocal heat transport modeling, meanfree path (stopping range) of nonlocal electrons with a wide range of kinetic energies is required instead of the use of locally defined thermal conductivity. Thus, the study of electron transport has recently undergone a transition to directly calculate how far nonlocal electrons traverse conductionzone plasmas before they thermalize with the background plasma. In Sec. V, we present the recent results about the chargedparticle stopping power of LDDrelevant plasmas by using timedependent densityfunctional theory (TDDFT) calculations. The global model of the meanfree path of nonlocal electrons in conductionzone CH plasmas, based on TDDFT calculations, is also discussed in this section. Finally, we conclude the review in Sec. VI and provide some perspectives on the challenges and future improvements of chargedparticle transport modeling for LDD fusion.
II. LDD PLASMA CONDITIONS FOR CHARGEDPARTICLE TRANSPORT MODELING
In this section, we give some basics about target geometry and laserpulse shapes that are currently used for LDD implosions on OMEGA^{16} and their scaleup for future symmetrical directdrive facilities having the current NIFscale laser energy ( $ E L\u223c2\u2009MJ$). Such examples of LDD targets and laser pulse shapes are displayed by Figs. 1(a) and 1(b), respectively, for OMEGAscale and NIFscale implosions. This type of LDD implosion design^{17–21} for OMEGA are often called “DTpushonDT” targets, in which an ∼40μmthick DT layer is covered by a thin CH ablator of ∼7–8 μm thickness. To mitigate the significant Rayleigh–Taylor instability growth from laser imprints^{22–26} and other target perturbation seeds,^{27–29} a highintensity laser picket is used to launch a strong shock into the target. The main steppulse laser [see Fig. 1(a)] drives a timed second shock to further compress and accelerate the target to give a highadiabat ( $\alpha =4\u20138$) and highvelocity ( $ v imp>400$ km/s) implosion. As usual the adiabat of the imploding shell is defined as the ratio of DT shell pressure ( $P)$ to the degeneracy Fermi pressure ( $ P F$) of the corresponding electron density, i.e., $\alpha =P/ P F$. The high implosion velocity tends to give high hotspot temperature and pressure for reaching the ignition threshold. It is noted that the “valley” of the main laser pulse is used to create some relaxation in the ablation front and to decrease the inflight aspect ratio (IFAR) for better implosion stability. In contrast to the ∼27 kJ laser energy on OMEGA, the scalingup target shown in Fig. 1(b) is for a future symmetrical directdrive laser facility which has the current NIFscale laser energy of ∼2 MJ. Overall, such an NIFscale LDD target has an outer diameter of $\u2205=4$ mm, which is roughly four times as big as the OMEGAscale target; The laser pulse shape is also stretched by a factor of ∼4 [see Fig. 1(b)], to obtain a hydroequivalent implosion. The two differentsize targets shown in Fig. 1 are close to be hydrodynamically equivalent, which means the onedimensional hydrodynamic simulation of one target can be scaled to the other situation by their size ratio. Note that some physics is not scalable, for instance, the αparticle heating in the NIFscale target will be absent in the OMEGAscale implosion because of their different hotspot sizes.
To give an idea of transportrelevant plasma density and temperature conditions encountered by LDD implosions, we use the onedimensional (1D) radiationhydrodynamic code LILAC^{30} to simulate both LDD targets as shown in Fig. 1. The 1D code LILAC was developed by the Laboratory for Laser Energetics at University of Rochester for LDD fusion designs and simulations. It invokes stateoftheart physics models for LDD fusion, which include the firstprinciples equationofstate (FPEOS) tables^{31–34} for both CH and DT, the radiation transport with the group diffusion method using firstprinciples opacity tables^{35,36} (FPOT) for LDD target materials, the raytracingbased laser absorption package of inversebremsstrahlung with crossbeam energy transfer (CBET^{15}), the thermal transport modeling of either a fluxlimited model^{37–40} or nonlocal models,^{41–43} as well as the $\alpha $particle energy deposition with stopping power models.^{44–48} As an example, the LILAC simulation results are presented in Figs. 2(a) and 2(b), respectively, for the OMEGAscale and NIFscale implosions. In these figures, the mass density (red curve) and plasma temperature (green curve) are plotted as a function of target radius for a time snapshot during the LDD implosion. Specifically, Fig. 2(a) gives the density and temperature profiles of the inflight capsule at the instant of $t=1.6$ ns amid the OMEGAscale implosion [see Fig. 1(a)]. Similarly, Fig. 2(b) illustrates the implosion of NIFscale target at $t=6.0$ ns. The density profile (red curve) in Fig. 2(a) shows that at this time the released shock has propagated to the location near the radius of $R\u2248200\u2009\mu m$, while peak density of the DT shell is around $R\u2248250\u2009\mu m$. Going further toward a large radius, one enters the coronal plasma regime in which the DT/CH density continues dropping and the temperature (green line) increases monochromatically. In Fig. 2(a), the blue dashdotted curve stands for the laser deposition in space; One sees that the critical density for the $\lambda =351$ nm laser is located around $R\u2248350\u2009\mu m$ at this moment, beyond that there is no laser deposition (laser beam reflected back). The shaded area, which starts from the critical density and ends at the ablation front, is the socalled conductionzone plasma region. The ablation front is conventionally defined at the outer point where the density drops to $1/e$fold of the peak density. In general, the conductionzone plasmas span the mass density range of $\rho \u22480.01\u20132$ g/cm^{3} and the corresponding temperatures of $ T e\u22482000\u201350$ eV. In this regime, the plasmas transition from a fully ionized classical plasma to partially ionized and moderately degenerate matter. In LDD implosions, the laser energy absorbed by electrons in the corona needs to be transported through the conductionzone plasma mainly by energetic electrons [ $v=(2\u20133)\xd7 v t$ with $ v t$ being the thermal velocity of electrons in coronal plasmas]. The heat carried by these energetic electrons is, therefore, transported to the outer side of the dense shell for ablation, which essentially determines lasertarget energy coupling in LDD.
Looking at the plasma conditions exemplified by Figs. 2(a) and 2(b), one sees that the conductionzone size varies from $ L C Z\u223c100\u2009\mu m$ for OMEGAscale target to $ L C Z\u223c200\u2013300\u2009\mu m$ for NIFscale implosions. The conductionzone plasmas can be made of ablator (CH), DT, and their mixtures. The spatial variation of temperature (green curve in Fig. 2) shows that a large gradient, i.e., $ T e$ quickly changes from ∼50 to 2000 eV within a few hundred micrometers. This large electron temperature gradient drives a large heat flux from the corona toward the ablation front of the imploding shell. If local thermal transport is assumed, one needs to know the local thermal conductivity ( $\kappa $) to compute the heat flux: $ Q H=\u2212\kappa \u2207 T e$. When the coronal temperature increases and/or the temperature gradient becomes very steep, nonlocal thermal transport needs to be invoked. In this case, the meanfree path (related to stopping range) of nonlocal electrons in conductionzone plasmas is required to simulate nonlocal heat transport. As mentioned above, the heatcarrying electrons will have a velocity of $two\u2009to\u2009three$ times the thermal velocity, which means they have kinetic energies of $ E K\u2248(4\u20139)\xd7k T e$. How these energetic electrons ( $\u223c3\u201330$ keV) traverse through the conductionzone plasmas is a key physics piece for accurately simulating lasertarget coupling and ablationdriven instability growth in LDD implosions.
Finally, electron thermal transport is a concern not only for the conductionzone plasmas from critical density to the ablation front (discussed above) but also for the hotspot formation in the DT shell stagnation stage. The latter situation is illustrated by Fig. 3 for the same targets discussed in Figs. 1 and 2. Similar to Fig. 2, the density and temperature profiles are plotted in Fig. 3 as a function of radius during the deceleration phase of the implosion, when the DT shell is starting to slow down due to the buildup of pressure inside the hotspot. At this moment [ $t=2.05$ ns in Fig. 3(a) and $t=8.62$ ns in Fig. 3(b)] a “return” shock (highdensity peak) is launched into the slowingdown DT shell; the temperature gradient in the hotspot is much steeper than the “outsider” conductionzone situation discussed in Fig. 2. Figure 3 shows that the electron temperature drops rapidly from $ T e=3\u20136$ keV in the center down to $ T e\u2248200$ eV in the stagnating dense DT shell, within a spatial distance of $\u223c20\u201340\u2009\mu m$ for the small OMEGAscale target and of $\u223c100\u2009\mu m$ in the NIFscale LDD implosion, respectively. Electron thermal conduction plays a critical role in the formation of the hotspot, as the same process not only determines heat loss from the hotspot but also drives a large amount of DT mass ablated into the core. In Fig. 3, the yellow region indicates the strong electron conduction encountered during the hotspot formation in LDD implosions (very similar to LID situation). In addition to electron thermal conduction, $\alpha $partcile stopping is another physics piece in modeling chargedparticle transports for LDD. The applicable region is marked in Fig. 3 to indicate the plasma condition that DT fusionproduced $\alpha $particles will deposit their energies to start bootstrap heating and subsequently for burnwave to propagate into the dense DT shell. In general, for the $\alpha $partcile stopping power, one cares about the following DT density and temperature conditions: $\rho =10\u20131000$ g/cm^{3} and $ T e=200\u201320\u2009000$ eV. In Sec. III, we shall review what plasma physics models had been developed over many decades for both electron thermal transport and $\alpha $partcile stopping in radhydro codes for LDD target designs and simulations.
III. TRADITIONAL PLASMA PHYSICS MODELS OF CHARGEDPARTICLE TRANSPORT FOR LDD SIMULATIONS
In radiationhydrodynamic simulations of LDD fusion implosions, thermal transport mainly by electrons is the key physics process for modeling laser energy coupling to the imploding capsule. The energy equation for each individual cell (with discrete spatial grid) of plasma fluid contains a source term called “heat flux,” defined as $ Q e=\u2212\kappa \u2207 T e$, under the approximation of local thermal conduction. Here, the thermal conductivity ( $\kappa $) is an important plasma property that is needed in ICF simulations. This quantity determines the heat transport in ICF plasmas and also affects the hydrodynamic instability growth in ICF implosions^{49} because the ablative stabilization of perturbation growth depends on the ablation velocity, which is a function of heat conduction.
Shortly after the introduction of the ICF concept^{1} in 1972, studies followed to determine the most appropriate models of thermal conductivity for moderatelytostrongly coupled and degenerate plasmas in the highdensity, lowtemperature regime. The Spitzer model^{50} of thermal conductivity κ, formulated in the 1950s for classical ideal plasmas, breaks down in this regime because the Coulomb logarithm for electron–ion collisions becomes negative. In the 1970s, Brysk et al.^{51} suggested that the Hubbard model^{52} of degenerate plasma can be “bridged” with the Spitzer model. However, the analytical formula given in the Brysk paper^{51} is only for the weakcoupling regime. In the 1980s, Lee and More^{53} applied Krook's model to the Boltzmann equation and derived a set of transport coefficients including κ. Meanwhile, Ichimaru and colleagues^{54} developed the socalled “Ichimaru model” of thermal conductivity for fully ionized plasmas, using the linear response theory. In addition, the averageatom model and its improved versions, such as the PURGATORIO package^{55} and the SCAALP model,^{56} have also been used to numerically calculate κ for materials interesting to ICF and astrophysics.
The modified Lee–More model of thermal conductivity discussed above has been implemented in LILAC for heat transport modeling of LDD implosions for decades before the 2010s, although it has resulted in some discrepancies when compared with experiments. For instance, Fig. 4 gives such an example in which the xray selfimaging technique^{58} was used to measure the spatial distribution of coronal plasma emissions. In Fig. 4(a) the schematic diagram of the xray selfimaging technique^{58} was presented, for which a framing xray camera with a pinhole and 1milBe filter records many frames of xray ( $h\nu \u22651$ keV) emissions from an imploding target. The top panels of Fig. 4(b) display two exampled xray images in 2D space from a typical cryogenic DT implosion on OMEGA. Specifically, these images were taken from shot #79626 in which the DT layer has a thickness of ∼67 $\mu m$ with a deuterated GDP ablator of ∼8.2 $\mu m$ thick. The target was driven by a veryhigh adiabat ( $\alpha \u224812)$ laser pulse shape with a highintensity picket plus a step main pulse of a total pulse duration of 2 ns. The images were taken at $t\u22481.72$ ns and $t\u22481.80$ ns during the implosion, with a timewindow averaging of ∼40ps. Similar to what Fig. 2 shows, the conductionzone transitions from CH to DT during the implosion of thin CHablator target. The xray emission peaks at a certain combination of electron density, electron temperature, and ion charge Z. The two peak “rings” of xray signals, shown by the top panels of Fig. 4(b), represent the emission from the CH/DT interface (outer ring) and the ablation front of the DT shell (inner ring). After azimuthal averaging of the top two images in Fig. 4(b), the xray emission signals as a function of target radius are plotted by black curves in the bottom panels of Fig. 4(b). The red dashed curves are from LILAC simulation using the modified Lee–More thermal conductivity model. One can see that if the inner DT peak is aligned between experiment and simulation, the outer CH/DT peaks disagree with each other. The simulated emission peak from the CH/DT interface moves to a larger distance than the experiment. This illustrates the possible need for improving the thermal conductivity model (among other pieces of physics) for LDD fusion simulations. The other possible physics pieces that might contribute to the discrepancy are given as follows: (1) The inaccuracy of laser absorption modeling in coronal plasmas particularly when CBET is at play; (2) any possible instability growth could cause the mixing of ablator into DT plasmas, even though this is less likely for such very high adiabat implosions ( $\alpha \u224812)$; and (3) any strong magnetic field created by instability growth could hinder nonlocal electron conduction along the radial direction at the ablation front, whose importance is still unknown for such highadiabat LDD implosions. Assessing these possible mechanisms, we currently believe that the inaccuracies of thermal conduction modeling and laser absorption are the two plausible scenarios to resolve the lasertarget coupling discrepancy between experiments and simulations.
IV. THERMALCONDUCTIVITY MODEL BASED ON QUANTUM MOLECULAR DYNAMICS PLUS KUBO–GREENWOOD CALCULATIONS
In the practice of using quantum molecular dynamics (QMD) plus Kubo–Greenwood formulas to compute electrical and thermal conductivities, we first run DFTbased quantum molecular dynamics simulations of HED plasmas for thousands of MD steps. The long QMD “trajectory” gives enough ionic configurations to sample a real HED plasma. From these ionic configurations, we can do a velocity–velocity correlation analysis to determine the correlation time ( $\tau )$ for the HED system. Once that is done, we can pick uncorrelated snapshots along the QMD trajectory by skipping certain MD steps (corresponding to the correlation time). Using these uncorrelated ionic configurations, we can then perform selfconsistentfield DFT calculations to obtain the Kohn–Sham energies, orbitals, and occupations for the calculation of Onsager coefficients [Eq. (4)]. Finally, the electrical and thermal conductivities can be obtained by using Eqs. (5) and (6) with averaging over all uncorrelated snapshots of QMD steps. Normally, 10 or 20 snapshots can give a good averaged conductivity value for HED plasmas. The QMDplusKubo–Greenwood calculations of thermal conductivity have been performed for a wide range of materials in the warmdense matter regime,^{61–66} as well as electrical/optical conductivities in superdense plasmas.^{67} In general, the quantum molecular dynamics method can be used to simulate any warmdense plasmas regardless how such HED plasmas are generated in experiments (by lasers, pulsed power machines, or static compression with diamond anvil cell) or in nature (e.g., astrophysical bodies such as white dwarfs). Quantum molecular dynamics simulations sample the thermodynamic equilibrium state of an HED plasma in a supercell with a periodic boundary condition by following the motions of ions in real time, while electron behavior is described quantum mechanically. Such firstprinciples simulations are normally limited by the number of atoms involved ( $N<10\u2009000$) and the plasma temperature ( $T< T F$; $ T F$ being the Fermi temperature).
Figures 5 and 6 give some examples of QMDplusKubo–Greenwood calculations of thermal conductivity of deuterium^{68} as a function of plasma temperature for mass densities of $\rho =2.453$ g/cm^{3} and $\rho =24.945$ g/cm^{3}, respectively. These firstprinciples QMD+KG calculations, based on which a global fitting model was developed,^{68} are compared to other conductivity models, such as the modified Lee–More,^{53} Ichimaru,^{54} and Hubbard models.^{52} In the warmdense matter regime (plasma temperatures less than 50–100 eV), the QMD+KG results of thermal conductivity are generally a factor of ∼3–10 higher than the modified Lee–More model (long dashed curve). The Ichimaru model gives a slightly higher thermal conductivity than that of QMD+KG calculations in this regime, while the Hubbard model gives an incorrect temperature dependence at the highT end. A similar trend is maintained for both deuterium densities, as shown in Figs. 5 and 6. The similar QMD+KG calculations have also been performed for polystyrene (CH) at a wide range of densities and temperatures.^{69} Some exampled results of κ for CH are plotted in Figs. 7(a) and 7(b) for two relevant CH densities of $\rho =1.05$ g/cm^{3} and $\rho =10.0$ g/cm^{3}, respectively. Again, a global conductivity model of CH plasmas^{69} was developed by using these firstprinciples calculations in combination with an ionization model based on the Thomas–Fermi–von Weizsacker averageatom model. Comparisons with a recent experiment^{70} and the modified Lee–More model^{53} are also made in Fig. 7, in which the higher conductivity from QMD+KG calculations is also witnessed for warmdense CH plasmas (similar to the D_{2} case). The experimental result was inferred from xray phasecontrast imaging of differentiated heating between CH and Be targets.^{70} Even though the experimental error bar is still too big to discriminate different models, the QMD+KG calculations provide reasonable agreement with the experiment, given that the electron–electron scattering effect (tending to reduce κ) is not fully included in QMD+KG calculations.^{71,72} It is also noted that the similar reduction effect of electrical conductivity by electron–electron (and dynamic electron–ion) collisions has been examined with using the extended electron force field method.^{73} Finally, it is noted that due to the exponential increase in DFT computation cost for high temperatures ( $\u221d T 3)$ these QMD+KG calculations of κ is only practical up to $T\u223c T F$; The hightemperature behavior of κ was out of the reach by using the Kubo–Greenwood formulism. One has to assume the classical model is correct for very high temperature (strictly speaking that is an unproven assumption); then an interpolation in between the lowT and highT limits was roughly made.
To illustrate how the QMD+KG conductivity model affects the LDD fusion simulations, we have used the fluxlimited $(f=0.06)$ local heat transport model in onedimensional (1D) LILAC to simulate a typical LDD implosion on OMEGA. Note that the stateoftheart heat transport model in LILAC has transitioned to a nonlocal one (discuss later); but testing the conductivity model can only be done with local transport modeling. The local transport results are displayed in Fig. 8, in which Fig. 8(a) gives the laser pulse shape and target geometry for an OMEGAscale LDD implosion. Again, the LILAC simulations used the local transport model with a flux limiter of $f=0.06$, invoking different models of thermal conductivity κ. Figures 8(b)–8(d) present the LILAC simulation results using the standard modified Lee–More conductivity model (red dashed curves) compared to the firstprinciples QMD+KG model of κ (blue solid curves) for both CH and DT (κ of DT is derived from κ of D_{2} by density scaling). Except for the different conductivity models, the two LILAC simulations apply the same inversebremsstrahlung plus CBET laser absorption model, the same firstprinciples equationofstate (FPEOS) tables,^{31–34} and the same firstprinciples opacity table^{35,36} (FPOT) for both the DT fuel and the CH ablator. The profiles of mass density and electron temperature, shown in Fig. 8(b) at the end of laser pulse ( $t=2.5$ ns), indicate that the two thermal conductivity models give somewhat different implosion dynamics. The higher conductivity inferred from the firstprinciples QMD+KG model (Figs. 5–7) implies slightly more laser energy coupling to the imploding target, explaining why the QMD+KG result (blue solid curve) shows a slightly faster moving shell with ∼8% higher density, compared to the standard simulation using the modified Lee–More conductivity model (red dashed curve). The imploding shell reaches its peak velocity at $t=2.66$ ns for the $ \kappa QMD + K G$ simulation and $t=2.69$ ns for the $ \kappa mLM$ case, respectively, due to different implosion dynamics. Figure 8(c) shows the mass density and adiabat profiles at their respective peak velocity instants from the two simulations. Once again, the shell conditions are different; the $ \kappa QMD + K G$ simulation gives an ∼10% lower adiabat for the imploding shell. This has a significant consequence when the shell comes to stagnate. As an example, Fig. 8(d) shows the hotspot pressure and stagnated shell density at the instant when neutron production peaks in the two simulations ( $t\u223c2.76$ ns for both). The same bang time for the two simulations is due to the fact that the QMD+KGsimulated implosion (30ps slower) takes a bit longer time to decelerate and to converge more [Fig. 8(d)] when compared to the case of the modified Lee–More model. One can see that the $ \kappa QMD + K G$ simulation has reached a peak pressure of ∼105 Gbar in contrast to only ∼95 Gbar in the standard $ \kappa mLM$ case. A slightly higher density and more spherical convergence are also witnessed in the simulation using the new $ \kappa QMD + K G$ conductivity model, leading to more neutron yields. More details about how other physics models affect LDD fusion simulations can be found in previous publications.^{74,75}
Since an ICF implosion is always an integrated experiment in which different physics pieces, such as laser absorption, thermal conduction, and Rayleigh–Taylor instability growth, are simultaneously playing their roles in determining the final target performance, it is difficult to isolate the effect of different thermal conduction models on ICF yield and compression from other physics processes. The conventional wisdom of the ICF community is to make sure each physics piece be the best model one can get from a firstprinciples point of view. To that end, we believe the QMD+KG model of thermal conductivity is the current stateoftheart for warmdense plasmas, which is worth including into radhydro codes for LDD simulations.
V. TIMEDEPENDENT DENSITYFUNCTIONAL THEORY (TDDFT) CALCULATIONS FOR STOPPING POWER/RANGE OF CHARGED PARTICLES
A. TDDFT calculations of proton stopping power in WDM
The success of ICF ignition critically relies on the αparticle stopping power of hot/denseDT plasmas in the hotspot that is assembled during the stagnation of an imploding shell. Ignition occurs as evidenced by selfheating of hotspot DT plasmas when the fusionproduced αparticle energy deposition rate exceeds the energy loss power by radiation and electron conduction. Plasma physics models^{44–48} of αparticle stopping power of DT plasmas have been previously developed for simulating ignition and burnwave propagation in ICF implosions. These stopping power models made certain approximations about the physics mechanism of charged particle slowing down in dense plasmas, varying from binary collisions to the dielectric response of an electron gas. Excitation of collective electron waves and degeneracy effects of the dense DT shell^{76} are often approximated in such models. Whether or not these αparticle stopping power models are sufficient enough to precisely predict the DT burn wave propagation into dense DT shell remains to be seen. Given the field of ICF is on the verge of reaching the highgain ( $G\u226b1$) “compressiveburn” regime, it is important to pin down the modeling accuracy of αparticle stopping power in DT burning plasmas.
As an example, Fig. 9 shows the TDOFDFT calculation of proton stopping power in warmdense beryllium ( $ \rho 0=1.78$ g/cm^{3} and $kT=32$ eV). It displays the electron density in the TDOFDFT simulation box, in which the energetic (1.5 MeV) proton projectile (as an example) is traveling through the background warmdense beryllium plasmas. The 2D images, representing the twodimensional YZplane cut of the 3D electron density at the projectile location in Xaxis, illustrate the proton position (big red spot) at the two distinct instants of $t\u224820$ as and $t\u224853.8$ as. Note that the electron density peaks behind the projectile so that the electric “drag” force is responsible for the slowing down of the positive projectile (proton), while electron density waves are excited ahead of the projectile. In such TDOFDFT simulations, periodic boundary conditions are applied to the simulation box. Once the projectile reaches the end of the box, the background plasma is replaced by a new snapshot of ion configurations so that the run will continue for a distance of ∼μm. By measuring the proton energy loss vs the distance it travels, we can derive the stopping power ( $dE/dx$) of warmdense Be plasmas for a given projectile energy. In Fig. 10(a), the proton energy is plotted as a function of the distance traveled, which can be fitted to obtain the stopping power, $dE/dx=\u22120.042$ MeV/μm, for a 1.5MeV proton moving in soliddensity Be plasma at $kT=32$ eV. Repeating such TDOFDFT calculations for different proton energies and snapshots, we can obtain discrete points of $dE/dx$ as a function of projectile velocity (energy). The fitted $dE/dx$ data from the TDOFDFT calculations can be compared to the proton stopping power in warmdense Be plasmas measured by an experiment^{87} conducted on OMEGA. The experiment used a glass capsule filled with D^{3}He gas to produce ∼15 MeV proton source as the probe beam. Its energy spectrum is shown by the orange bars in Fig. 10(b). In this experiment, the solid Be cylinder of ∼532μm length was isochorically heated by x rays from laser directdrive of silver coating that covers the Be cylinder. Previous xray Thomas scattering measurements^{88} inferred the electron temperature of $kT\u223c32$ eV for the same platform. When the 15MeV proton source passes through the warmdense Be target, the deceleration proton spectra were recorded by a CR39 detector in the experiment. Using the obtained stopping power curve derived from our TDOFDFT calculations for a wide range of proton energies (from 1.5 to 15 MeV), we can take the same proton source to compute its slowdown spectrum. The results are compared in Fig. 10(b), which showed good agreement between experiment and the TDOFDFT calculations (green bars). In addition, two predictions from the LiPetrasso (LP) model^{46} and the Brown–Preston–Singleton (BPS) model^{47} are also given in Fig. 10(b), for which the ionization degree is taken from the Thomas–Fermi averageatom model. The two models overestimate the stopping power by ∼10%–20% when compared to TDOFDFT calculations and experimental measurements. The experimental error of ∼50keV in the energy measurement of slowingdown protons is small enough to discriminate these stopping power models. It is noted that in the TDOFDFT calculations there is no need to assume any ionization degree, as the method will not discriminate bound electrons from free electrons.
Next, we discuss a recent tabletop stoppingpower experiment^{89} which used short pulses to generate both the probe beam of protons and the warmdense carbon plasma as the target. In this experiment,^{89} the warmdense matter (WDM) target was created by irradiating an 1μmthick carbon foil using a 200fs Ti:sapphire laser pulse of 0.5 J energy. The probing proton beam is produced by the main laser beam of ∼4 J and 30fs focusing onto a 3μmthick aluminum foil. The target normal sheath acceleration (TNSA) mechanism produces a broadband spectrum of protons with a cutoff energy of 4 MeV. A specifically developed magnetic filtering device was used to select a monoenergetic pencillike proton beam of around 500 keV energy as the projectiles to pass through the WDM target for stopping power measurements. The laser heater beam was focused to a spot diameter of 300 μm, which is much larger than the probing proton beam spot size (50 μm). More details of the experiment can be found in Ref. 89.
Different from other stopping power experiments, this tabletop one pushed the measurement closer to the Bragg peak, where stopping power has its highest value. The warmdense carbon target has an average mass density of $\rho =0.5$ g/cm^{3} and $kT=10$ eV (inferred from radhydro simulations), while the energy of proton beam was selected to be ∼500 keV. For the experimental condition, the TDDFT calculations of proton stopping powers are compared in Fig. 11(a) with other plasma stoppingpower models. It shows that both TDOFDFT and TDKSDFT calculations give ∼20% smaller stopping power than most analytical models (except for the coldmaterial stopping model—SRIM). In the experiment, the proton energy loss in the sample was measured on shots with the heater beam driving the target at respective time delays of −316 ± 100, −116 ± 100, and 86 ± 100 ps relative to the onset of the heater laser pulse on the sample. The results are compared in Fig. 11(b), in which the measured energy loss is plotted as a function of the time delay between the proton beam and heater beam for creating the WDM target. The latest delay probes the WDM target fully, which gives an energy loss of ΔE_{WDM} = 39 ± 5 keV that is at least 15 keV lower than the classical predictions (orange lines). The experimental data are also compared with the results of the TDKSDFT energyloss calculations ΔE_{TDDFT} (green lines). The energy loss predicted by TDDFT calculations at the time of proton probing in WDM ΔE_{TDDFT} = 51 ± 2.5 keV is closer to the experiment than the classical mode predictions, even though the TDDFT result is still 22.7% ± 14% higher than the measured one.
B. TDDFT calculations for meanfree path of nonlocal electrons
In Sec. IV we described the thermal conductivity calculations by using the method combining QMD with the linear response theory of Kubo–Greenwood. However, these conductivity models built from QMD+KG calculations are only applicable for local heat transport modeling for LDD simulations. It has been realized that as the coronal temperature increases to $kT=3\u20135$ keV, electron transport may become more nonlocal.^{41–43} Namely, once the meanfree path of coronal electrons becomes longer than the local scale length of temperature gradient, electrons can deposit their energies nonlocally. In such cases, nonlocal heat transport has to be invoked for LDD simulations,^{41–43} which means the meanfreepath of nonlocal electrons is needed instead of local thermal conductivity κ. This transition from local to nonlocal heat transport in radhydro codes occurred about 15 years ago for LDD simulations, which made these QMD+KG thermal conductivity models less applicable for the nonlocal transport in LDD radhydro codes.
To improve the applicability of our QMD+KG thermal conductivity models, we have recently put an effort to directly calculate the stopping range (meanfree path) of nonlocal electrons in the conductionzone plasmas, using the same TDDFT method. For polystyrene (CH) plasmas, a global model of meanfree path of nonlocal electrons^{90} has been established through TDOFDFT and timedependent stochastic densityfunctional theory (TDsDFT) calculations^{82} by using energetic nonlocal electrons as projectiles. To give an example, the TDDFTcalculated electron meanfree path ( $ \lambda e)$ is plotted as a function of nonlocal electron energy in Fig. 12, for the conductionzone CH plasma of $\rho =0.5$ g/cm^{3} and $kT=100$ eV. The TDDFT results showed the relationship of $ \lambda e$ vs electron kinetic energy ( $ K E$) has a different slope when compared with the modified Lee–More model that was used as a standard in our radhydro codes for LDD simulations. The two curves crossover at $ K E\u22483$ keV. For highenergy nonlocal electrons of $ K E=25$ keV, the electron meanfree path from TDDFT calculations is almost a factor ∼2 longer than what the modified Lee–More model predicts. These differences in $ \lambda e$ can have a significant implication on simulations of lasertarget coupling for LDD implosions. Now, the similar effort has been extending to study nonlocal electron meanfree path in DT plasmas. The longer meanfree path of nonlocal electrons from TDDFT predictions implies more laser energy coupling to the imploding target if the same laser absorption maintains, leading to a faster implosion trajectory.^{90} To rematch the measured implosion trajectory, the laser absorption model with CBET needs to be recalibrated once the nonlocal thermal conduction model is updated by using TDDFTcalculated $ \lambda e$. Again, the integrated ICF modeling naturally relies on different physics pieces interplaying together to match experimental observations.
VI. CONCLUSION AND OUTLOOK
In this review, we have presented a historical perspective on the modeling of chargedparticle transport for laser directdrive (LDD) fusion. The transport of electrons and αparticles are of particular concern for LDD simulations, as the former critically determines the lasertarget coupling and the latter controls the bootstrapping heating for ignition. For electron thermal conduction from coronal plasmas to the ablation front, local heat transport relies on the thermal conductivity (κ) of LDD plasmas (CH and DT). Historically, the modified Lee–More model was used for κ in radhydro codes for simulating LDD implosions. Recent developments in building firstprinciples thermal conductivity models for warmdense CH and D_{2}/DT plasmas have been reviewed. These models were based on firstprinciples calculations through quantum molecular dynamics (QMD) simulations using densityfunctional theory (DFT) in combination with the Kubo–Greenwood linear response theory. Some exampled results are given to illustrate how these new electron thermal conductivity models affect LDD simulations when local heat transport modeling is invoked. Since the modeling of heat transport for LDD fusion has transitioned from the local approximation to the more accurate nonlocal electron transport, an effort has most recently been initiated to directly calculate the meanfree path (stopping range) of nonlocal electrons with timedependent densityfunctional theory (TDDFT). For that purpose, improvements to both the Kohn–Sham and orbitalfree implementations of the TDDFT method have occurred in the past 5 years. The TDOFDFT applications for calculating stopping power of heavy charged particles (protons) in warmdense matter have shown some preliminary success in giving good comparison with experimental measurements, even though there is some approximation to the noninteracting free energy functional in the orbitalfree formalism. Most recently, the development of the more accurate timedependent stochastic densityfunctional theory (TDsDFT),^{82} which is based on the Kohn–Sham formalism, is ready for ab initio calculations of chargedparticle stopping power in hightemperature and dense LDD plasmas.
Given the great potential of LDD in inertial fusion energy and highyield applications in SSP, further improvements in chargedparticle transport modeling will benefit the reliable design and simulation of LDD targets. A list of challenges and opportunities for future research is outlined here, with the hope of facilitating the ongoing progress in chargedparticle transport studies of HED plasmas:^{91,92}

Using the TDsDFT method to calculate the nonlocal electron stopping range (meanfree path) for DT plasmas in a wide range of densities and temperatures, which can be used to build a global meanfree path model for nonlocal electron heat transport. Namely, many TDDFT calculations for $ \lambda e$ should be performed at different plasma densities ( $\rho $), temperatures ( $T$), and nonlocal electron energies ( $ K nle$). Then a fitting formula of $ \lambda e = \lambda e ( \rho , T ; K nle)$, by parametrizing these TDDFT results, can be implemented into radhydro codes for the modeling of nonlocal heat transport.

Using the TDsDFT method to compute αparticle stopping power in hotspot and dense DT shell conditions so that an accurate αparticle stoppingpower model can be established for radhydro codes to reliably simulate burnwave propagation in highgain LDD fusion targets.

Investigating the electron–electron scattering effect^{71–73,93} on the electron meanfree path that might be partially missed in TDDFT calculations.

Exploring if highlevel theories, such as dynamic meanfield theory (DMFT) or manybody perturbation theory, beyond DFT can be used to improve calculations of chargedparticle transport properties for LDD plasmas.

Further developing/improving quantum kinetic theory, guided by the above ab initio calculations, to derive a reliable and fast method for calculating chargedparticle transport properties onthefly in radhydro codes.

Future precision experiments to measure the thermal conductivity and stopping power of charged particles in the ICFrelevant plasma regimes will provide benchmarks for the physics models developed for fusion applications.

Finally, machine learning (ML) and artificial intelligence (AI) will be explored to guide the development of global transport models for LDD simulations. The preliminary application of ML/AI to LDD target designs has been documented recently.^{20,94,95} In addition, ML/AI can also be used to bridge microscopic to macroscopic physics in lasermatter interactions.^{96}
ACKNOWLEDGMENTS
This material is based upon work supported by the Department of Energy (National Nuclear Security Administration) University of Rochester “National Inertial Confinement Program” under Award No. DENA0004144. The Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001).
This report was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.
AUTHOR DECLARATIONS
Conflict of Interest
The authors have no conflicts to disclose.
Author Contributions
Suxing Hu: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Project administration (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal). Katarina A. Nichols: Data curation (equal); Investigation (equal); Writing – review & editing (equal). Nathaniel R. Shaffer: Investigation (equal); Writing – review & editing (equal). Brennan Arnold: Investigation (equal). Alexander James White: Investigation (equal); Methodology (equal); Writing – review & editing (equal). Lee Collins: Investigation (equal); Methodology (equal); Writing – review & editing (equal). Valentin V. Karasiev: Investigation (equal). Shuai Zhang: Investigation (equal); Writing – review & editing (equal). Valeri N. Goncharov: Investigation (equal). Rahul Shah: Investigation (equal). Deyan I. Mihaylov: Investigation (equal). Sheng Jiang: Investigation (equal). Yuan Ping: Investigation (equal).
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author upon reasonable request.