Ab Initio Molecular Dynamics Simulations Research Articles

Anion-Dependent Strength Scale of Interactions in Ionic Liquids from X-ray Photoelectron Spectroscopy, Ab Initio Molecular Dynamics, and Density Functional Theory.

Using a combination of experiments and calculations, we have gained new insights into the nature of anion-cation interactions in ionic liquids (ILs). An X-ray photoelectron spectroscopy (XPS)-derived anion-dependent electrostatic interaction strength scale, determined using XPS core-level binding energies for IL cations, is presented here for 39 different anions, with at least 18 new anions included. Linear correlations of experimental XPS core-level binding energies for IL cations with (a) calculated core binding energies (ab initio molecular dynamics (AIMD) simulations were used to generate high-quality model IL structures followed by single-point density functional theory (DFT) to obtain calculated core binding energies), (b) experimental XPS core-level binding energies for IL anions, and (c) other anion-dependent interaction strength scales led to three main conclusions. First, the effect of different anions on the cation can be related to ground-state interactions. Second, the variations of anion-dependent interactions with the identity of the anion are best rationalized in terms of electrostatic interactions and not occupied valence state/unoccupied valence state interactions or polarizability-driven interactions. Therefore, the XPS-derived anion-dependent interaction strength scale can be explained using a simple electrostatic model based on electrostatic site potentials. Third, anion-probe interactions, irrespective of the identity of the probe, are primarily electrostatic, meaning that our electrostatic interaction strength scale captures some inherent, intrinsic property of anions independent of the probe used to measure the interaction strength scale.

High-Performance Dendrite-Free Lithium Metal Anode Based on Metal-Organic Framework Glass.

The performance of lithium metal batteries is severely hampered by uncontrollable dendrite growth and volume change within the anode. This work addresses these obstacles by introducing a novel strategy: applying an isotropic and internal grain-boundary-free layer, specifically, a metal-organic framework (MOF) glass layer with nano-porosity onto the electrochemically plated lithium metal anode. Both ab initio and classical molecular dynamics simulations indicate that the MOF glass layer makes the lithium transport smooth and uniform via its internal monolithic and interfacial advantages. This MOF glass layer with the fast and more uniform lithium diffusion in the monolithic interior and its interface enables dendrite-free lithium plating and stripping through surface confinement effect and interfacial effect. When employed in symmetric batteries, the achieved Li metal anode can operate over 300h at 1mAcm-2. The full batteries matched with LiFePO4 exhibit high capacity (148mAhg-1), excellent rate performance (61mAhg-1 at 5C), and outstanding cycling stability (with capacity retention of ≈90% after 1000 cycles). The full batteries matched with high-voltage LiCoO2 also show superior performances. Therefore, the strategy of utilizing a MOF glass layer enables the development of high-performance lithium metal anodes.

Li-decorated C48B12 and Li12C48B12-impregnated MOF-5 for hydrogen storage: A multi-scale simulation study

In this study, we employed density functional theory (DFT) to investigate the adsorption sites and quantities of Li atoms on the C48B12 surface. Additionally, we examined the hydrogen adsorption properties of the formed Li12C48B12 through ab initio molecular dynamics (AIMD) simulations. Our AIMD simulations confirmed the stability of adsorbed Li atoms on the C48B12 surface, computed adsorbed H2 quantities, and predicted gravimetric densities of H2 adsorption on Li12C48B12 at 77 K and 298 K. Grand Canonical Monte Carlo simulations explored the H2 gravimetric densities of pure MOF-5, C48B12-impregnated MOF-5, and Li12C48B12-impregnated MOF-5 at 77 K and 298 K within 100 bar. Li doping on the C48B12 surface did not significantly enhance the gravimetric densities of H2 in MOF-5 but enhanced the stability of H2 adsorption in MOF-5.

Revisiting thermodynamics in (LiF, NaF, KF, CrF2)–CrF3 by first-principles calculations and CALPHAD modeling

The thermodynamic description of the (LiF, NaF, KF, CrF2)–CrF3 systems has been revisited, aiming for a better understanding of the effects of Cr on the FLiNaK molten salt. First-principles calculations based on density functional theory (DFT) were performed to determine the electronic and structural properties of each compound, including the formation enthalpy, volume, and bulk modulus. DFT-based phonon calculations were carried out to determine the thermodynamic properties of compounds, for example, enthalpy, entropy, and heat capacity as functions of temperature. Phonon-based thermodynamic properties show a good agreement with experimental data of binary compounds LiF, NaF, KF, CrF3, and CrF2, establishing a solid foundation to determine thermodynamic properties of ternary compounds as well as to verify results estimated by the Neumann-Kopp rule. Additionally, DFT-based ab initio molecular dynamics (AIMD) simulations were employed to predict the mixing enthalpies of liquid salts. Using DFT-based results and experimental data in the literature, the (LiF, NaF, KF, CrF2)–CrF3 system has been remodeled in terms of the CALculation of PHAse Diagrams (CALPHAD) approach using the modified quasichemical model with quadruplet approximation (MQMQA) for liquid. Calculated phase stability in the present work shows an excellent agreement with experiments, indicating the effectiveness of combining DFT-based total energy, phonon, and AIMD calculations, and CALPHAD modeling to provide the thermodynamic description in complex molten salt systems.

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations.

In Situ Species Analysis of a Lithium-Ion Battery Electrolyte Containing LiTFSI and Propylene Carbonate.

In this study, we analyzed the species in a model electrolyte consisting of a lithium salt, lithium bis(trifluoromethane sulfone)imide (LiTFSI), and a widely used neutral solvent propylene carbonate (PC) with excess infrared (IR) spectroscopy, ab initio molecular dynamics simulations (AIMD), and quantum chemical calculations. Complexing species including the charged ones [Li+(PC)4, TFSI-, TFSI-(PC), TFSI-(PC)2, and Li(TFSI)2-] are identified in the electrolyte. Quantum chemical calculations show strong Li+···O(PC) interaction, which suggests that Li+ would transport in the mode of solvation-carriage. However, the interaction energy of each hydrogen bond in TFSI-(PC) is very weak, suggesting that TFSI- would transport in hopping mode. In addition, the concentration dependences of the relative population of the species were also derived, providing a scenario for the dissolving process of the salt in PC. These in-depth studies provide physical insights into the structural and interactive properties of the electrolyte of lithium-ion batteries.

DFT and AIMD insights into heterogeneous dissociation of 2-chlorothiophenol on CuO(111) surface: Impact of H2O and OH

Copper oxides are vital catalysts in facilitating the formation of polychlorinated thianthrenes/dibenzothiophenes (PCTA/DTs) through heterogeneous reactions in high-temperature industrial processes. Chlorothiophenols (CTPs) are the most crucial precursors for PCTA/DT formation. The initial step in this process is the metal-catalyzed production of chlorothiophenoxy radicals (CTPRs) from CTPs via dissociation reactions. This work combines density functional theory (DFT) calculations with ab initio molecular dynamics (AIMD) simulations to explore the formation mechanism of the adsorbed 2-CTPR from 2-CTP, with the assistance of CuO(111). Our study demonstrates that flat adsorption configurations of 2-CTP on the CuO(111) surface are more stable than vertical configurations. The CuO(111) surface acts as a strong catalyst, facilitating the dissociation of 2-CTP into the adsorbed 2-CTPR. Surface oxygen vacancies enhance the adsorption of 2-CTP on the CuO(111) surface, while moderately suppressing the dissociation of 2-CTP. More importantly, water molecules and surface hydroxyl groups actively promote the dissociation of 2-CTP. Specifically, water directly participates in the reaction through “water bridge”, enabling a barrier-free process. This research provides molecular-level insights into the heterogeneous generation of dioxins with the catalysis of metal oxides in fly ash from static and dynamic aspects, providing novel approaches for reducing dioxin emissions and establishing dioxin control strategies.

CO Intermediate‐Assisted Dynamic Cu Sintering During Electrocatalytic CO2 Reduction on Cu−N−C Catalysts

AbstractThe electrochemical CO2 reduction reaction (eCO2RR) to multicarbon products has been widely recognized for Cu‐based catalysts. However, the structural changes in Cu‐based catalysts during the eCO2RR pose challenges to achieving an in‐depth understanding of the structure–activity relationship, thereby limiting catalyst development. Herein, we employ constant‐potential density functional theory calculations to investigate the sintering process of Cu single atoms of Cu−N−C single‐atom catalysts into clusters under eCO2RR conditions. Systematic constant‐potential ab initio molecular dynamics simulations revealed that the leaching of Cu−(CO)x moieties and subsequent agglomeration into clusters can be facilitated by synergistic adsorption of H and eCO2RR intermediates (e.g., CO). Increasing the Cu2+ concentration or the applied potential can efficiently suppress Cu sintering. Both microkinetic simulations and experimental results further confirm that sintered Cu clusters play a crucial role in generating C2 products. These findings provide significant insights into the dynamic evolution of Cu‐based catalysts and the origin of their activity toward C2 products during the eCO2RR.

Finding Natural, Dense, and Stable Frustrated Lewis Pairs on Wurtzite Crystal Surfaces for Small‐Molecule Activation

AbstractThe surface frustrated Lewis pairs (SFLPs) open up new opportunities for substituting noble metals in the activation and conversion of stable molecules. However, the applications of SFLPs on a larger scale are impeded by the complex construction process, low surface density, and sensitivity to the reaction environment. Herein, wurtzite‐structured crystals such as GaN, ZnO, and AlP are found for developing natural, dense, and stable SFLPs. It is revealed that the SFLPs can naturally exist on the (100) and (110) surfaces of wurtzite‐structured crystals. All the surface cations and anions serve as the Lewis acid and Lewis base in SFLPs, respectively, contributing to the surface density of SFLPs as high as 7.26×1014 cm−2. Ab initio molecular dynamics simulations indicate that the SFLPs can keep stable under high temperatures and the reaction atmospheres of CO and H2O. Moreover, outstanding performance for activating the given small molecules is achieved on these natural SFLPs, which originates from the optimal orbital overlap between SFLPs and small molecules. Overall, these findings not only provide a simple method to obtain dense and stable SFLPs but also unfold the nature of SFLPs toward the facile activation of small molecules.

CO Intermediate-Assisted Dynamic Cu Sintering During Electrocatalytic CO2 Reduction on Cu-N-C Catalysts.

The electrochemical CO2 reduction reaction (eCO2RR) to multicarbon products has been widely recognized for Cu-based catalysts. However, the structural changes in Cu-based catalysts during the eCO2RR pose challenges to achieving an in-depth understanding of the structure-activity relationship, thereby limiting catalyst development. Herein, we employ constant-potential density functional theory calculations to investigate the sintering process of Cu single atoms of Cu-N-C single-atom catalysts into clusters under eCO2RR conditions. Systematic constant-potential ab initio molecular dynamics simulations revealed that the leaching of Cu-(CO)x moieties and subsequent agglomeration into clusters can be facilitated by synergistic adsorption of H and eCO2RR intermediates (e.g., CO). Increasing the Cu2+ concentration or the applied potential can efficiently suppress Cu sintering. Both microkinetic simulations and experimental results further confirm that sintered Cu clusters play a crucial role in generating C2 products. These findings provide significant insights into the dynamic evolution of Cu-based catalysts and the origin of their activity toward C2 products during the eCO2RR.

Finding Natural, Dense, and Stable Frustrated Lewis Pairs on Wurtzite Crystal Surfaces for Small-Molecule Activation.

The surface frustrated Lewis pairs (SFLPs) open up new opportunities for substituting noble metals in the activation and conversion of stable molecules. However, the applications of SFLPs on a larger scale are impeded by the complex construction process, low surface density, and sensitivity to the reaction environment. Herein, wurtzite-structured crystals such as GaN, ZnO, and AlP are found for developing natural, dense, and stable SFLPs. It is revealed that the SFLPs can naturally exist on the (100) and (110) surfaces of wurtzite-structured crystals. All the surface cations and anions serve as the Lewis acid and Lewis base in SFLPs, respectively, contributing to the surface density of SFLPs as high as 7.26×1014 cm-2. Ab initio molecular dynamics simulations indicate that the SFLPs can keep stable under high temperatures and the reaction atmospheres of CO and H2O. Moreover, outstanding performance for activating the given small molecules is achieved on these natural SFLPs, which originates from the optimal orbital overlap between SFLPs and small molecules. Overall, these findings not only provide a simple method to obtain dense and stable SFLPs but also unfold the nature of SFLPs toward the facile activation of small molecules.

CAIMD Simulations Guided Design of Atomic Praseodymium Doping In–Bi Nanofibers for High‐Energy‐Efficiency CO2 Electrolysis to Formate in Ultra‐Wide Potential Window

AbstractThe electrochemical CO2 reduction reaction (ECO2RR) has emerged as a promising technology for achieving carbon neutralization. Even though considerable efforts are dedicated to gain deep insight into the understanding of ECO2RR on a mechanism level through density functional theory (DFT) studies, effects of solvent molecules and temperature have long been neglected by conventional DFT calculations as a consequence of limitations in current technologies and computational power of supercomputers. Under this context, the lack of comprehensive understanding over the energy changes in the reaction derived from the only concern on free energy changes between reaction intermediates have arouse an urgent call for exploring feasible calculation options toward generalized theoretical study. Here, a systematic mechanism study is provided toward ECO2RR via constrained ab initio molecular dynamics (cAIMD) simulations, in which the effects of solvent water molecules and temperature are taken into consideration to guide the synthesis of single‐atom alloy (SAA) catalyst. Consequently, the resultant Pr0.05/InBi achieves a maximum Faradic efficiency (FE) of 96.4% and an energy efficacy (EE) of 59.41% for formate. This work offers a novel approach to the design and screening of SAA catalysts, presenting foreseeable future in accelerating the industrial application process of ECO2RR.

A holistic understanding of optical properties in amorphous H-terminated Si-nanostructures: Combining TD-DFT with AIMD

Silicon, traditionally known as an indirect band gap semiconductor, unveils intriguing properties at the nanoscale, stemming from deviations from k-conservation rules within nanostructures. In our study, we scrutinized four hydrogenated Si 0D-nanostructures—Si10H16, Si14H20, Si18H24, and Si22H28—to unravel their dynamic stability under thermal fluctuations and optical characteristics. We initiated our exploration by employing the TD-DFT framework to generate and analyze the optical properties of these geometrically optimized nanostructures. Simultaneously, we conducted ab initio molecular dynamics simulations to examine the structural robustness and thermal stability of the four structures. Leveraging the Car-Parrinello molecular dynamics approach within the Quantum ESPRESSO open software suite, we observed temperature evolution and stability differences among the nanostructures at targeted temperatures 40 and 300 K. Our subsequent investigation delved into the Turbo-Lanczos time-dependent DFT method, unraveling the optical properties and excited-state dynamics of hydrogenated Si nanostructures. The results unveiled shifts towards higher energy absorption edges E0, accompanied by alterations in the permittivity tensor, complex refractive index, oscillator strength, and reflectivity. Notably, the analysis revealed an enlarged HOMO-LUMO gap, distinctive from bulk Si. Furthermore, our models predicted the elimination of phase-dependent E1/E2 optical transition peaks in the imaginary part of the dielectric function, and a gradual decrease in the low-frequency dielectric response with increased hydrogenation of the amorphous structures. These findings underscore the promising applications of hydrogenating Si nanostructures in diverse technological domains such as optoelectronics, memristors, sensors, and quantum computing. Their tunable optical properties, size-dependent behaviors, and compatibility with existing silicon-based devices make them particularly appealing for next-generation technologies.

An investigation of self-interstitial diffusion in α-zirconium by an on-the-fly machine learning force field

The on-the-fly machine learning force field approach, based on the Gaussian approximation potential and Bayesian error estimation, was used to study the diffusion of self-interstitial atoms in α-zirconium. Ab initio molecular dynamics simulations of lattice vibration and interstitial diffusion at different temperatures were employed to develop the force field. The radial and angular descriptors of the potential were further optimized to achieve better agreement with first-principles results. Subsequent long-term diffusion simulations were performed to assess the diffusion behavior based on the obtained force field. Tracer diffusion coefficients and diffusion anisotropy were studied at temperatures of 600–1200 K, and the Bayesian errors were estimated throughout the diffusion simulations. The mean and maximum estimated Bayesian errors of atomic force were approximately twice as large as those observed during the learning period. The basal diffusion was greatly favored compared to the interstitial diffusion along the c-axis, consistent with previous simulations based on first-principles results and classical potentials. The accuracy and applicability of the current on-the-fly machine learning approach were critically evaluated.

High capacity hydrogen storage structure based on a two-dimensional material (Ga2C6) with strong adsorption properties

The use of solid-state devices is a promising approach to address the current technological requirements for sustainable clean energy applications. Within this context, it has become apparent that density functional theory (DFT) has emerged as a powerful tool to study intrinsic electronic characteristics and shows great potential to lead the way to novel materials exploration. Here, we have investigated H2 adsorption on Ga2C6 monolayer, a prospective model for the storage of H2 molecule because of its structural stability. The hydrogen molecules favor being adsorbed at the carbon hexagon center with a binding energy of 0.39 eV. The thermodynamic stability of the Ga2C6 monolayer upon adsorption of 22H2 molecules was evaluated at room temperature using ab-initio molecular dynamics (AIMD) simulations. It is found from the electronic structure analysis that the semiconducting Ga2C6 sheet maintain its behavior after 22H2 molecules were adsorbed. Owing to the small activation energy of about 0.0198 eV, the process of H2 molecule migration at the surface easy proceeds. This investigation demonstrates a strong (GC) gravimetric capacity around 9.65 wt%, and a desorption temperature (TD) around 213.68 K. The above findings imply that the Ga2C6 monolayer may serve as a suitable medium for hydrogen adsorption.

The Superior Electrochemical Property of LiTFSI@(Ba+Nb)-co-doped Li7La2.75Ba0.25Zr1.75Nb0.25O12@PEO Composite Membrane in All-Solid-State Lithium Battery

It is popular to choose the cubic phase Li7La3Zr2O12(c-LLZO) to synthesize the PEO-based complex membrane in all-solid-state battery. In this work, Ba/Nb elements were selected to achieve Li6.75La3Zr1.75Nb0.25O12 (LLZNO) and Li7La2.75Ba0.25Zr1.75Nb0.25O12 (LLBZNO) with high ion conductivity, which effectively enhance the electrochemical properties of the complex solid membrane and the all-solid-state battery. The higher ion mobility of LLBZNO compared to that of LLZNO and LLZO is calculated by ab-initio molecular dynamics (AIMD) simulation and further confirmed by experiments. Results show that the cubic phase of LLBZNO is more stable than that of LLZNO. The complex membrane of LLBZNO@ polyethylene oxide (PEO) (CPE-2-x wt%) exhibits the better electrochemical properties than LLZNO@PEO (CPE-1-x wt%) complex membrane. In detail, the CPE-2–10 wt% has the outstanding lithium transfer number ( tLi+ ) of 0.63, as well as the excellent compatibility with lithium metal anode. In addition, at 0.1 mA∙cm−2, the lithium/CPE-2–10 wt%/lithium symmetric cell remains stable after 1000 h. It is LLBZNO that makes LLBZNO@PEO composite polymer electrolyte(CPE) the potential for the application in all-solid-state lithium battery.

Exploring Li-Ion Transport Properties of Li3TiCl6: A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li3TiCl6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li3TiCl6. Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li3TiCl6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials.

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate abinitio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces.

Functionalization of Cathode–Electrolyte Interface with Ionic Liquids for High-Performance Quasi-Solid-State Lithium–Sulfur Batteries: A Low-Sulfur Loading Study

We introduce a quasi-solid-state electrolyte lithium-sulfur (Li–S) battery (QSSEB) based on a novel Li-argyrodite solid-state electrolyte (SSE), Super P–Sulfur cathode, and Li-anode. The cathode was prepared using a water-based carboxymethyl cellulose (CMC) solution and styrene butadiene rubber (SBR) as the binder while Li6PS5F0.5Cl0.5 SSE was synthesized using a solvent-based process, via the introduction of LiF into the argyrodite crystal structure, which enhances both the ionic conductivity and interface-stabilizing properties of the SSE. Ionic liquids (IL) were prepared using lithium bis(trifluoromethyl sulfonyl)imide (LiTFSI) as the salt, with pre-mixed pyrrolidinium bis(trifluoromethyl sulfonyl)imide (PYR) as solvent and 1,3-dioxolane (DOL) as diluent, and they were used to wet the SSE–electrode interfaces. The effect of IL dilution, the co-solvent amount, the LiTFSI concentration, the C rate at which the batteries are tested and the effect of the introduction of SSE in the cathode, were systematically studied and optimized to develop a QSSEB with higher capacity retention and cyclability. Interfacial reactions occurring at the cathode–SSE interface during cycling were also investigated using electrochemical impedance spectroscopy, cyclic voltammetry, and X-ray photoelectron spectroscopy supported by ab initio molecular dynamics simulations. This work offers a new insight into the intimate interfacial contacts between the SSE and carbon–sulfur cathodes, which are critical for improving the electrochemical performance of quasi-solid-state lithium–sulfur batteries.