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At least 397 records · Page 22

Electrode Elastic Modulus as the Dominant Factor in the Capping Effect in Ferroelectric Hafnium Zirconium Oxide Thin Films

The discovery of ferroelectricity in hafnia based thin films has catalyzed significant research focused on understanding the ferroelectric property origins and means to increase stability of the ferroelectric phase. Prior studies have revealed that biaxial tensile stress via an electrode “capping effect” is a suspected ferroelectric phase stabilization mechanism. This effect is commonly reported to stem from a coefficient of thermal expansion (CTE) incongruency between the hafnia and top electrode. Despite reported correlations between ferroelectric phase fraction and electrode CTE, the thick silicon substrate dominates the mechanics and CTE-related stresses, negating any dominant contribution from an electrode CTE mismatch toward the capping effect. In this work, these discrepancies are reconciled, and the origin of these differences deriving from electrode elastic modulus, not CTE, is demonstrated. Pt/M/TaN/Hf 0.5 Zr 0.5 O 2 /TaN/Si devices, where M is platinum, TaN, iridium, tungsten, and ruthenium, were fabricated. Sin 2 (ψ)-based X-ray diffraction measurements of biaxial stress in the HZO layer reveal a strong correlation between biaxial stress, remanent polarization, and electrode elastic modulus. Conversely, a low correlation exists between the electrode CTE, HZO biaxial stress, and remanent polarization. A higher elastic modulus enhances the resistance to electrode elastic deformation, which intensifies the capping effect during crystallization, and culminates in the tandem restriction of out-of-plane hafnia volume expansion and preferential orientation of the polar c-axis normal to the plane. These behaviors concomitantly increase the ferroelectric phase stability and polarization magnitude. This work provides electrode material selection guidelines toward the development of high-performing ferroelectric hafnia into microelectronic devices, such as nonvolatile memories.

36 MATERIALS SCIENCE↗

Improving commercial truck fleet composition in emission modeling using 2021 US VIUS data

Commercial trucks are essential elements of the nation's supply chain system. Meanwhile, intensive truck movements contribute significantly to system externalities, such as energy use and air pollution. However, collecting detailed fleet composition and distribution of operational patterns remains a barrier to accurately accounting for these impacts. The recently released 2021 US Vehicle Inventory and Use Survey (US VIUS) fills a critical gap in understanding commercial truck fleet distributions, their operations, and business constraints at the national scale. This study aims to understand the latest US commercial vehicle fleet composition and operational characteristics using 2021 US VIUS data and calibrate the fleet inputs in regulatory emission models to assess the potential emission implications of the VIUS-derived fleet composition. The emission rates for commercial trucks and default fleet composition are collected from the U.S. EPA's MOtor Vehicle Emission Simulator (MOVES4). The 2021 US VIUS data is applied to improve fleet characteristics such as the long-haul fraction and the vehicle mileage accumulation rate. The study also investigates potential emission reduction benefits under various forecasted fleet electrification scenarios. The energy consumption and critical air pollutant rates by vehicle types are compared between MOVES4 and US VIUS fleets for both current and future scenarios to provide insights into the latest U.S. commercial vehicle fleet characteristics and their implications on energy and emissions. This study helps policymakers and practitioners advance the commercial fleet generation for emission models. It also deepens the understanding of the emission reduction potential of the commercial fleet under various fleet projections.

2021 US VIUS↗

Vacancy Spectroscopy of Non-Abelian Kitaev Spin Liquids

Spin vacancies in the non-Abelian Kitaev spin liquid are known to harbor Majorana zero modes, potentially enabling topological quantum computing at elevated temperatures. Here, we study the spectroscopic signatures of such Majorana zero modes in a scanning tunneling setup where a non-Abelian Kitaev spin liquid with a finite density of spin vacancies forms a tunneling barrier between a tip and a substrate. In this study, our key result is a well-defined peak close to zero bias voltage in the derivative of the tunneling conductance whose voltage and intensity both increase with the density of vacancies. This “quasi-zero-voltage peak” is identified as the closest analog of the zero-voltage peak observed in topological superconductors that additionally reflects the fractionalized nature of spin-liquid-based Majorana zero modes. We further highlight a single-fermion Van Hove singularity at a higher voltage that reveals the energy scale of the emergent Majorana fermions in the Kitaev spin liquid. Our proposed signatures are within reach of current experiments on the candidate material α-RuCl 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Synthesis of Hydrophilic Derivative Surfactants From Algae-Derived Unsaponifiable Lipids

In the context of decarbonizing the economy, the utilization of biologically sourced feedstocks to produce replacements for petroleum-derived materials is becoming more urgent. Improving renewable biomass production and utilization is imperative for commercializing future biorefineries. Algae-derived biomass is a particularly promising feedstock thanks to its attractive oil content and composition; specifically, the high-value products in the unsaponifiable lipids have not been included in a conversion process. Here we demonstrate surfactant synthesis from a complex oil fraction as the hydrophobic donor moieties, yielding products that are similar to commercially available surfactants such as the linear alkyl benzene sulfonates. Unsaponifiable lipids extracted from algae were derivatized to non-ionic surfactants using a green chemical synthesis route based on a double esterification with succinic acid and polyethylene glycol. The in-depth molecular and structural surfactant characterization is included and indicates that the resulting properties fall between those of pure cholesterol and phytol used as surrogates for the reaction synthesis demonstration. This is the first demonstration of an effective and potentially high-value synthesis of functional surfactants with properties that can be tailored based on the relative composition of the resulting hydrocarbon alcohol components in the mixture. This novel green chemistry synthesis approach provides a route to high-value product synthesis from algae.

09 BIOMASS FUELS↗

A Joint Roman Space Telescope and Rubin Observatory Synthetic Wide-Field Imaging Survey

We present and validate 20 deg(2) of overlapping synthetic imaging surveys representing the full depth of the Nancy Grace Roman Space Telescope High-Latitude Imaging Survey (HLIS) and 5 yr of observations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The two synthetic surveys are summarized, with reference to the existing 300 deg(2) of LSST simulated imaging produced as part of Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2). Both synthetic surveys observe the same simulated DESC DC2 universe. For the synthetic Roman survey, we simulate for the first time fully chromatic images along with the detailed physics of the Sensor Chip Assemblies derived from lab measurements using the flight detectors. The simulated imaging and resulting pixel-level measurements of photometric properties of objects span a wavelength range of similar to 0.3-2.0 mu m. We also describe updates to the Roman simulation pipeline, changes in how astrophysical objects are simulated relative to the original DC2 simulations, and the resulting simulated Roman data products. We use these simulations to explore the relative fraction of unrecognized blends in LSST images, finding that 20-30 per cent of objects identified in LSST images with i-band magnitudes brighter than 25 can be identified as multiple objects in Roman images. These simulations provide a unique testing ground for the development and validation of joint pixel-level analysis techniques of ground- and space-based imaging data sets in the second half of the 2020s - in particular the case of joint Roman-LSST analyses.

gravitational lensing↗

Collisional stopping power of ions in warm dense matter

Here, a model for the collisional stopping of ions on free electrons in warm dense matter is developed and explored. It is based on plasma kinetic theory, but with modifications to address the warm dense matter regime. Specifically, it uses the Boltzmann-Uehling-Uhlenbeck kinetic equation to incorporate effects of Fermi degeneracy of electrons. The cross section is computed from quantum scattering of electrons and ions occurring via the potential of mean force derived from an average atom model, which incorporates effects of strong Coulomb correlations. Predictions from this model show comparable accuracy to results from time-dependent density functional theory calculations for deuterium near solid density and a temperature of several electronvolts, at a fraction of the computational cost. Further, the model captures the transition of a plasma from the classical limit to the degenerate limit, including qualitative behaviors of solid state theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

HUNTRESS: a fast heuristic for reconstructing phylogenetic trees of tumor evolution (HUNTRESS) v0.1

We introduce HUNTRESS (Histogrammed UNion Tree REconStruction heuriStic), a computational method for tumor phylogeny reconstruction from noisy genotype matrices derived from single-cell sequencing data, whose running time is linear with the number of cells and quadratic with the number of mutations. Provided that the input genotype matrix includes no false positives, each cellular subpopulation is at least a user defined fraction of the total number of cells, and the number of cells are much bigger than the number of mutations considered, HUNTRESS computes the ground truth tumor phylogeny with high probability. On simulated data HUNTRESS is faster than available alternatives with comparable or better accuracy. Additionally, the phylogenies reconstructed by HUNTRESS on two single-cell sequencing data sets agree with the best known evolutionary scenarios for the associated tumors.

Buluc, Aydin↗

Upcycling of Waste Plastics into Carboxylic Acids for Biodegradable Surfactants

This work outlines a process for producing high‐purity (>95%) carboxylate surfactants from post‐consumer recycled high‐density polyethylene (PCR‐HDPE). The approach involves the thermal depolymerization of PCR‐HDPE via pyrolysis, followed by fractional distillation to isolate C9–C14 olefins. These olefins undergo hydroformylation using cobalt carbonyl catalysts to generate aldehydes, which are subsequently oxidized to carboxylic acids using Pinnick oxidation under mild aqueous‐phase conditions. Neutralization of the resulting carboxylic acids with sodium hydroxide produces plastic‐derived carboxylate surfactants (PDCs) in the form of sodium carboxylates. Subsequent purification steps ensure surfactant‐grade purity and enable accurate assessment of physicochemical properties. The resulting PDCs are evaluated for critical micelle concentration (CMC), foamability, surface tension reduction, and calcium ion tolerance, demonstrating competitive behavior with conventional anionic carboxylate surfactants. This route provides a sustainable alternative for surfactant production, reducing reliance on fossil‐derived feedstocks and valorizing plastic waste streams through chemical upcycling.

Biodegradable surfactants↗

Solvent selection for biphasic extraction of 5-hydroxymethylfurfural via multiscale modeling and experiments

We introduce a comprehensive conceptual framework for selecting solvents for reactive extraction in biphasic organic-water systems and demonstrate it for the separation of HMF (5-hydroxylmethylfurfural), a platform chemical produced in the acid-catalyzed dehydration of hexoses. We first perform in silico screening of ~2500 solvents, from the ADFCRS-2018 database using the ADF COSMO-RS implementation, and classification, based on the solvent partition coefficient. We then determine experimentally the partition coefficients for HMF, fructose, and products of HMF rehydration (levulinic acid (LA), and formic acid (FA)), the mutual water-organic solvent solubilities, and the separation factors in >50 select solvents spanning multiple homologous series at room temperature and a typical reaction temperature with in situ sampling. We find that COSMO-RS is excellent for screening purposes (typical error in most cases within a factor of ~2). Increased temperatures lead to significant reduction in partitioning, and room temperature measurements are clearly inadequate for solvent selection. Upon down selecting classes of solvents based on separation performance, we perform experimental thermal stability and reaction compatibility studies of a small set of solvents at relevant reactive-extraction temperatures. We discover that many substituted phenols exhibit an order-of-magnitude increase in partitioning compared to conventional solvents due chiefly to hydrogen bond interactions and show the necessary stability but retain a significant fraction of water and LA, factors that need to be considered in technoeconomic analysis. In contrast, anilines, aldehydes, and acids are good to excellent regarding separation but incompatible with this specific reaction medium. Furthermore, this multifaceted framework can be extended to other biomass-derived products and processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Upcycling plastic waste into polyhydroxyalkanoates with high carbon conversion via CO 2 plasma-enabled deconstruction

Plastic deconstruction into fermentable intermediates is a key step for microbial bio-upcycling into value-added products. In this study, CO 2 plasma deconstruction was used as an electrified route to convert polyethylene into oxygenated intermediates and liquid (OIL). The resulting OIL was rich in fatty acids, fatty alcohols, and hydrocarbons, making it a suitable feedstock for medium-chain-length polyhydroxyalkanoate (mcl-PHA) production by Pseudomonas putida NRRL B-14688 and Pseudomonas resinovorans NRRL B-2649. Compared with batch fermentation and monocultures, fed-batch co-cultivation markedly improved biomass formation and PHA accumulation, likely due to complementary substrate utilization, particularly hydrocarbon conversion by P. resinovorans. Using virgin polyethylene-derived OIL (Vir-OIL), the co-culture achieved 40.11% mcl-PHA content and about 18% PHA yield based on total OIL fed. More importantly, OIL produced from post-consumer single-use plastic films (PCR-OIL) was directly fermented and well supported the cell growth and PHA accumulation, achieving 38.11% PHA content and about 14.7% PHA yield. Based on emulsified OIL fractions, PHA yields for Vir-OIL and PCR-OIL were comparable (∼28%). PHA granules were extracted from PCR-OIL-grown cells with high recovery (88.25%) and purity (94.8%). Five monomers were identified in the polymer, including 3-hydroxyhexanoate (3HHx), 3-hydroxyoctanoate (3HO), 3-hydroxydecanoate (3HD), 3-hydroxydodecanoate (3HDD), and 3-hydroxytetradecanoate (3HTD), with 3HO (44.28%) and 3HD (40.60%) as the dominant units. The polymer exhibited moderate molecular weight and narrow dispersity (M n = 65.4 kDa, M w = 92.1 kDa, Đ = 1.40) and low crystallinity (T m ≈ 76.6 °C, X c ≈ 15.5%). These characteristics indicate elastomer-like behavior, making the material suitable for flexible applications such as films, coatings, adhesives, and blend modifiers. Overall, this study establishes a CO 2 plasma-assisted route for generating fermentable polyethylene-derived intermediates and demonstrates that fed-batch co-culture fermentation can effectively funnel plastic-derived carbon into mcl-PHA.

42 ENGINEERING↗

Near-cancellation of up- and down-gradient momentum transport in forced magnetized shear-flow turbulence

Visco-resistive magnetohydrodynamic turbulence, driven by a two-dimensional unstable shear layer that is maintained by an imposed body force, is examined by decomposing it into dissipationless linear eigenmodes of the initial profiles. The down-gradient momentum flux, as expected, originates from the large-scale instability. However, continual up-gradient momentum transport by large-scale linearly stable but nonlinearly excited eigenmodes is identified and found to nearly cancel the down-gradient transport by unstable modes. The stable modes effectuate this by depleting the large-scale turbulent fluctuations via energy transfer to the mean flow. This establishes a physical mechanism underlying the long-known observation that coherent vortices formed from nonlinear saturation of the instability reduce turbulent transport and fluctuations, as such vortices are composed of both the stable and unstable modes, which are nearly equal in their amplitudes. The impact of magnetic fields on the nonlinearly excited stable modes is then quantified. Furthermore, even when imposing a strong magnetic field that almost completely suppresses the instability, the up-gradient transport by the stable modes is at least two-thirds of the down-gradient transport by the unstable modes, whereas for weaker fields, this fraction reaches up to 98%. These effects are persistent with variations in magnetic Prandtl number and forcing strength. Finally, continuum modes are shown to be energetically less important, but essential for capturing the magnetic fluctuations and Maxwell stress. A simple analytical scaling law is derived for their saturated turbulent amplitudes. It predicts the falloff rate as the inverse of the Fourier wavenumber, a property which is confirmed in numerical simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Symmetry and scaling in one-dimensional compressible two-phase flow

Investigations of shock compression of heterogeneous materials often focus on the shock front width and overall profile. The number of experiments required to fully characterize the dynamic response of a material often belie the structure–property relationships governing these aspects of a shock wave. Recent observations measured a pronounced shock-front width on the order of 10 s of ns in particulate composites. We focus on particulate composites with disparate densities and investigate whether the mechanical interactions between the phases are adequate to describe this emergent behavior. The analysis proceeds with a general Mie–Grüneisen equation of state for the matrix material, a general drag force law with general power-law scaling for the particle-matrix coupling of the phases, and a volume fraction-dependent viscosity. Lie group analysis is applied to one-dimensional hydrodynamic flow equations for the self-consistent interaction of particles embedded in a matrix material. The particle phase is characterized by a particle size and volume fraction. The Lie group analysis results in self-similar solutions reflecting the symmetries of the flow. The symmetries lead to well-defined scaling laws, which may be used to characterize the propagation of shock waves in particle composites. An example of the derived scaling laws for shock attenuation and rise time is shown for experimental data on shock-driven tungsten-loaded polymers. A key result of the Lie analysis is that there is a relationship between the exponents characterizing the form of the drag force and the exponent characterizing the shock velocity and its attenuation in a particulate composite. Comparison to recent experiments results in a single exponent that corresponds to a conventional drag force.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Improving helium abundance determinations with Leo P as a case study

Currently, the primordial helium abundance is best estimated through spectroscopic observations of H II regions in metal-poor galaxies. However these determinations are limited by several systematic uncertainties which ultimately limit our ability to accurately ascertain the primordial abundance. In this study, we improve the methodologies for solving for the reddening, the emission contributions from collisional excitation of the H I atoms, the effects underlying absorption in the H I and He I emission lines, and the treatment of the blended H I and He I emission at λ3889 with the aim of lowering the systematic uncertainties in helium abundance determinations. To apply these methods, we have obtained observations of the He I λ10830 emission line in the brightest H II region in the extremely metal-poor (3% Z ⊙ ) galaxy Leo P with the LUCI1 instrument on the LBT. We combine this measurement with previous MODS/LBT observations to derive an improved helium abundance. In doing so, our present analysis results in a decrease in the uncertainty in the helium abundance of Leo P by approximately 70%. Furthermore, this result is combined with data from other observations to estimate the primordial helium mass fraction, Yp= 0.2453 ± 0.0034.

79 ASTRONOMY AND ASTROPHYSICS↗

Skeletal Kinetics Reduction for Astrophysical Reaction Networks

A novel methodology is developed to extract accurate skeletal reaction models for nuclear combustion. Local sensitivities of isotope mass fractions with respect to reaction rates are modeled based on the forced optimally time-dependent (f-OTD) scheme. These sensitivities are then analyzed temporally to generate skeletal models. The methodology is demonstrated by conducting skeletal reduction of constant density and temperature burning of carbon and oxygen relevant to Type Ia supernovae (SNe Ia). The 495-isotopes Torch model is chosen as the detailed reaction network. A map of maximum production of 56 Ni in SNe Ia is produced for different temperatures, densities, and proton-to-neutron ratios. The f-OTD simulations and the sensitivity analyses are then performed with initial conditions from this map. A series of skeletal models are derived and their performances are assessed by comparison against currently existing skeletal models. Previous models have been constructed intuitively by assuming the dominance of α-chain reactions. The comparison of the newly generated skeletal models against previous models is based on the predicted energy release and 44 Ti and 56 Ni abundances by each model. The consequences of ye ≠ 0.5 in the initial composition are also explored where ye is the electron fraction. The simulated results show that 56 Ni production decreases by decreasing ye as expected, and that the 43 Sc is a key isotope in proton and neutron channels toward 56 Ni production. It is shown that an f-OTD skeletal model with 150 isotopes can accurately predict the 56Ni abundance in SNe Ia for ye ≲ 0.5 initial conditions.

79 ASTRONOMY AND ASTROPHYSICS↗

Effects of MOF linker rotation and functionalization on methane uptake and diffusion

The flexible degrees of freedom in metal–organic frameworks (MOFs) can have significant effects on guest molecule behavior. However, in the majority of studies applying molecular simulations to MOFs, the framework is assumed to be rigid in order to minimize computational cost. Here, in this work, we assess the significance of this assumption on a representative example of methane uptake and diffusion in UiO-66. We introduce an open-source code to modify MOFs through functionalization and linker rotation and we perform Grand Canonical Monte Carlo and molecular dynamics simulations of methane in each of the functionalized and linker-rotated derivatives of UiO-66. We find that linker rotation moderately influences methane uptake and significantly influences methane diffusion. Our assessment provides ranges of property values that serve as measures of uncertainty of these two properties associated with linker rotation. We further determine that void volume fraction and minimum pore size are the features that govern methane uptake and diffusion, respectively. These findings illustrate the impact of linker rotation on MOFs and provide design principles to guide future investigations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The cosmic DANCe of Perseus

Context. Star-forming regions are excellent benchmarks for testing and validating theories of star formation and stellar evolution. The Perseus star-forming region, being one of the youngest (< 10 Myr), closest (280-320 pc), and most studied in the literature, is a fundamental benchmark. Aims. We aim to study the membership, phase-space structure, mass, and energy (kinetic plus potential) distribution of the Perseus star-forming region using public catalogues (Gaia, APOGEE, 2MASS, and Pan-STARRS). Methods. We used Bayesian methodologies that account for extinction to identify the Perseus physical groups in the phase-space, retrieve their candidate members, derive their properties (age, mass, 3D positions, 3D velocities, and energy), and attempt to reconstruct their origin. Results. We identify 1052 candidate members in seven physical groups (one of them new) with ages between 3 and 10 Myr, dynamical super-virial states, and large fractions of energetically unbounded stars. Their mass distributions are broadly compatible with that of Chabrier for masses ≳0.1 M ⊙ and do not show hints of over-abundance of low-mass stars in NGC 1333 with respect to IC 348. These groups’ ages, spatial structure, and kinematics are compatible with at least three generations of stars. Future work is still needed to clarify if the formation of the youngest was triggered by the oldest. Conclusions. The exquisite Gaia data complemented with public archives and mined with comprehensive Bayesian methodologies allow us to identify 31% more members than previous studies, discover a new physical group (Gorgophone: 7 Myr, 191 members, and 145 M ⊙ ), and confirm that the spatial, kinematic, and energy distributions of these groups support the hierarchical star formation scenario.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Selection of high-redshift Lyman-Break Galaxies from broadband and wide photometric surveys

Here, in this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) or the Vera C. Rubin Legacy Survey of Space and Time (LSST), using a Random Forest algorithm. This work is conducted in the context of future large-scale structure spectroscopic surveys like DESI-II, the next phase of the Dark Energy Spectroscopic Instrument (DESI), which will start around 2029. We use deep imaging data from the Hyper Suprime Camera (HSC) and the Canada-France-Hawaii Telescope Large Area U-band Deep Survey (CLAUDS) on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the u,g,r,i and z bands to UNIONS depth. The Random Forest algorithm is trained with the u,g,r,i and z bands to classify LBGs in the 2.5 < z < 3.5 range. We find that fixing a target density budget of 1,100 deg -2 , the Random Forest approach gives a density of z > 2 targets of 873 deg -2 , and a density of 493 deg -2 of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg 2 . We predict uncertainties on Alcock-Paczynski parameters α ⊥ and α ∥ to be 0.7% and 1% for 2.6 < z < 3.2, resulting in a potential 2% measurement of the dark energy fraction at high redshift. Additionally, we estimate the uncertainty in local non-Gaussianity and predict σ fNL ≈ 7, which would be comparable to the current best precision achieved by Planck. The latter forecast suggests that achieving the precision required to place stringent constraints on inflationary models (σ fNL ≈ 1) using spectroscopic galaxy surveys necessitates the development of a next-generation (Stage V) spectroscopic survey.

cosmological parameters from LSS↗

Quantum Ornstein-Zernike theory for two-temperature two-component plasmas

Laboratory plasma production almost always preferentially heats either the ions or electrons, leading to a two-temperature state. In this state, density functional theory molecular dynamic simulation is the state of the art for modeling bulk material properties. We construct a statistical mechanics model for the two temperature limit that is theoretically consistent with the molecular dynamics method. We proceed to derive the electron-ion multi-temperature quantum Ornstein-Zernike equations for the first time. This allows the construction of a two-temperature two-component plasma model using the average atom from which we can compute bulk material properties at a fraction of the computation time of the two-temperature density functional theory simulation. The accuracy of the model is benchmarked against ion pair correlation and self-diffusion results from ab initio simulation. Here, we proceed to compute the viscosity and ion thermal conductivity as a function of both ion and electron temperature.

Ab initio molecular dynamics↗