Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials such as ionic conductors, fuel cells, and gas sensors, yet most existing studies and software tools extract only standard metrics, leaving much of the information contained in the trajectories unused. Here we introduce GEMDAT, a user-friendly Python toolkit for site-resolved diffusion analysis of MD simulations of solid-state materials (https://github.com/GEMDAT-repos/GEMDAT). Beyond mean-squared displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT provides jump rates, attempt frequencies, site-specific activation energies, rotational diffusion, and percolation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies—quantities that are also directly comparable to experimental diffraction data. Migration sites can be defined manually or identified automatically from the trajectory. A built-in caching approach, together with rapid visualization capabilities, makes the workflow fast and interactive. We demonstrate GEMDAT on a series of case studies involving crystalline Li- and Na-ion conductors, plastic crystals, amorphous structures, and surface configurations, showing how the code extracts atomic-level structural features and connects them to macroscopic transport properties, thereby guiding the optimization and development of solid-state materials.

Silicon (Si) anodes promise high energy density for all-solid-state batteries (ASSBs), while avoiding Li-metal dendrites. However, one of the key limitations is the initial loss of active Li, reflected in a low initial Coulombic efficiency (ICE), arising from solid electrolyte (SE) decomposition, lithium trapping, and native oxide conversion. Here, we report an (electro)chemically guided interfacial-engineering strategy leveraging an irreducible SE to construct a dynamic nanoscale Si|SE interface that enhances ICE. The intrinsic stability of the SE down to the working potentials of Si anodes eliminates decomposition, whereas mechanochemical mixing activates interfacial reactions that realize in situ prelithiation. The engineered interface manifests efficient ion transport and replenishes Li inventory through redox reactions, while stabilizing electrochemical performance. Our approach delivers a record-high ICE approaching 100% in Si half-cells, together with >95% in high-loading LiCoO₂ full cells, demonstrating that precise interface control can unlock the full potential of Si anodes for high-energy ASSBs.

Solid–solid interfaces play a critical role in the performance of energy storage systems, catalysts, and electronic devices, yet their atomic-scale structure and behaviour remain challenging to probe experimentally. Explicit atomistic modelling provides a powerful approach for investigating interfaces by directly constructing and simulating contacting phases, enabling the calculation of interfacial energetics, electronic structure, charge transfer, stability, and transport processes. This Review summarises the methodology and applications of explicit interface modelling, including interface construction strategies, properties accessible from atomistic simulations, and recent advances enabled by machine-learned interatomic potentials that extend simulations to larger time and length scales. We also discuss how computational results can be interpreted and validated using experimental characterisation techniques, and highlight current challenges and emerging opportunities in predictive interface modelling and materials design.

Solid-state batteries currently receive extensive attention due to their potential to outperform lithium-ion batteries in terms of energy density when featuring next-generation anodes such as lithium metal or silicon. However, most highly conducting solid electrolytes decompose at the low operating voltages of next-generation anodes leading to irreversible lithium loss and increased cell resistance. Such performance losses may be prevented by designing electrolytes which are thermodynamically stable at low operating voltages (anolytes). Here, we report on the discovery of a new family of irreducible (i.e., fully reduced) electrolytes by mechanochemically dissolving lithium nitride into the Li₂S antifluorite structure, yielding highly conducting crystalline Li_2+xS_1-xN_x phases reaching >0.2 mS cm^-1 at ambient temperature. Combining impedance spectroscopy experiments and ab initio density functional theory calculations we clarify the mechanism by which the disordering of the sulfide and nitride ions in the anion sublattice boosts ionic conductivity in Li_2+xS_1-xN_x phases by a factor 10⁵ compared to the Li₂S host structure. This advance is achieved through a novel theoretical framework, leveraging percolation analysis with local-environment-specific activation energies and is widely applicable to disordered ion conductors. The same methodology allows us to rationalize how increasing nitrogen content in Li_2+xS_1-xN_x antifluorite-like samples leads to both increased ionic conductivity and lower conductivity-activation energy. These findings pave the way to understanding disordered solid electrolytes and eliminating decomposition-induced performance losses on the anode side in solid-state batteries.

Fluorination of electrolytes has been a widely used strategy to enable stable cycling in lithium metal batteries. However, a move toward fluorine-free electrolytes is desirable given the safety and environmental concerns surrounding fluorinated materials. Designing these electrolytes requires a comprehensive understanding of bulk electrolyte and interfacial properties in the absence of fluorine, particularly the solvation structures surrounding Li⁺ and the solid electrolyte interface (SEI) composition. Among fluorine-free Li salts, lithium nitrate (LiNO₃) is often used to obtain highly ion-conductive SEI components. However, its poor ion dissociation and rapid consumption upon freshly plated lithium currently hinder its use as the main electrolyte salt. Herein, we show that the modification of Li⁺ inner solvation structures by employing lithium bis(oxalato)borate (LiBOB) as the secondary salt in LiNO₃/diglyme electrolytes synergistically improves both bulk Li⁺ transport and SEI properties. It significantly enhances ion dissociation, which increases the ionic conductivity of the electrolyte despite an increase in its viscosity. Furthermore, the presence of LiBOB-derived outer SEI components over the LiNO₃-derived ion-conductive inner SEI layer results in low-surface-area Li deposits and lower Li⁺/anion consumption during cycling. The dual-salt fluorine-free electrolyte enables stable, long-term cycling in Li/Cu cells for >700 cycles and shows promising capacity retention in Li/LFP full cells at ambient temperature. Our work highlights the importance of tuning the Li⁺ solvation structures for optimizing bulk and interface properties in fluorine-free electrolytes and presents a viable pathway toward the development of greener electrolytes for lithium metal batteries.

By varying the bromine content and cooling method, we are able to induce site disorder in the Li_6-xPS_5-xBr_1+x (x = 0, 0.3, 0.5) system via two routes, allowing us to disentangle the impact of site disorder and chemical composition on conductivity. Through solid-state nuclear magnetic resonance (NMR), we can explore the chemical environment as well as short-range lithium-ion dynamics and compare these to results obtained from neutron diffraction and electrochemical impedance spectroscopy (EIS). We find that the cooling method has a profound effect on the ⁷Li and ³¹P environment that cannot be explained through 4d site disorder alone. The configurational entropy (S_conf) is used as a more complete descriptor of structural disorder and linked to distortions in both the phosphorus and lithium environment. These distortions are correlated to increased intercage movement through ⁷Li T₁ spin-lattice relaxation (SLR) NMR. Further analysis of the prefactors obtained from SLR NMR and EIS allows us to obtain the migrational entropy (ΔS_m). For short-range SLR movement, the ΔS_m correlates well with S_conf, implying that increased intercage movement is related to distortion of the lithium cages as well as a decrease of the intercage distance. Comparison to EIS shows that an increase in short-range movement translates into increased long-range movement in a straightforward manner for slow-cooled samples. However, for quench-cooled samples, this correlation is lost. Lattice softness and phonon-ion interactions are suggested to play an important role in long-range conduction which only becomes apparent when chemical composition and disorder are disentangled. This work shows that by altering one synthesis step, the relationship between site-occupancy-based descriptors (site disorder or S_conf) and lithium dynamics is changed profoundly. Furthermore, it shows that chemical composition and descriptors of site disorder cannot be seen as one and the same, as both play a role that changes with the length scale probed. Finally, it challenges the implicit assumption that increased short-range diffusivity automatically results in increased long-range diffusivity.

All-solid-state batteries receive ample attention due to their promising safety characteristics and energy density. The latter holds true if they are compatible with next-generation high-capacity anodes, but most highly ion-conductive solid electrolytes decompose at low operating potentials, leading to lithium loss and increased cell resistances. Here the dynamic stability of solid electrolytes that can improve all-solid-state battery performance is demonstrated. Halide electrolytes Li₃YCl₃Br₃ and Li₂ZrCl₆, considered unstable at low potentials, are found to exhibit structurally reversible redox activity beyond their electrochemical stability windows, increasing compatibility with anodes and contributing to capacity without compromising ionic conductivity. The benefit of this dynamic stability window is demonstrated with cost-effective red phosphorus anodes, resulting in high reversible capacities (2,308 mAh g⁻¹), high rate capacity retention (1,024 mAh g⁻¹ at 7.75 mA cm⁻²) and extended cycle life (61% retention after 1,780 cycles). Furthermore, high areal capacity (7.65 mAh cm⁻²) and stability (70% retention after 1,000 cycles) are achieved for halide-based full cells with red phosphorous anodes. The beneficial redox activity of halide electrolytes greatly expands their application scenarios and suggests valuable battery design principles to enhance performance.

Irreducible solid electrolytes (SEs), characterized by non-Li framework ions in their lowest oxidation states, offer intrinsic compatibility with low-reduction-potential, high-capacity negative electrodes, such as lithium metal and silicon. In these SE materials, disorder engineering and vacancy formation reduce lithium-ion diffusion barriers, achieving room-temperature ionic conductivities exceeding 0.1 mS cm^–1. Experiments and atomistic simulations confirm that irreducible SEs form decomposition-free interfaces with Li metal. Their limited oxidative stability can be addressed by pairing them with an electrolyte layer stable with practical cathodes yet demanding interface compatibility between the two electrolyte layers. Here we highlight key research directions to accelerate irreducible SE transition from laboratory to practical application, including expanding compositional diversity, optimizing interfaces with cathode-facing electrolytes, developing scalable thin-film processing, and exploring compatibility with other low working potential anodes like silicon. Addressing these challenges is essential to unlock the full potential of irreducible SEs for high-energy, long-life, all-solid-state batteries.

Due to their high ionic conductivity, lithium-ion conducting argyrodites show promise as solid electrolytes for solid-state batteries. Aliovalent substitution is an effective technique to enhance the transport properties of Li₆PS₅Br, where aliovalent Si substitution triples ionic conductivity. However, the origin of this experimentally observed increase is not fully understood. Our density functional theory (DFT) study reveals that Si⁴⁺ substitution increases Li diffusion by activating Li occupancy in the T4 sites. Redistribution of Li-ions within the lattice results in a more uniform distribution of Li around the T4 and neighboring T5 sites, flattening the energy landscape for diffusion. Since the T4 site is positioned in the intercage jump pathway, an increase in the intercage jump rate is found, which is directly related to the macroscopic diffusion and bulk conductivity. Analysis of neutron diffraction experiments confirms partial T4 site occupancy, in agreement with the computational findings. Understanding the aliovalent substitution effect on interstitials is crucial for improving solid electrolyte ionic conductivity and advancing solid-state battery performance.

Deep eutectic solvents (DESs) represent an environmentally friendly alternative to conventional organic solvents. Their liquid range determines the areas of application, and therefore, the prediction of solid-liquid equilibrium (SLE) diagrams is essential for developing new DESs. Such predictions are not yet possible by using the current state-of-the-art computational models. Herein, we present an alternative model based on support vector regression integrating experimental data, a conductor-like screening model for real solvents simulations, and cheminformatic descriptors for predicting melting temperatures of binary metal-free DESs or ionic liquids, allowing the researcher to estimate the eutectic formation and SLE for specific combinations of components. The model was developed based on the manually collected database of 1648 mixture melting temperatures for 237 experimentally described DESs, and its accuracy was demonstrated by 5-fold cross-validation (R² ∼ 0.8). The presented machine learning methodology empowers researchers to predefine the liquid range of the mixture and holds promise for efficient molecular combination screening, facilitating the discovery of tailored DESs for desired applications from catalysis and extraction to energy storage. By enabling a deeper understanding of DES behavior and the targeted design of these solvents, the proposed approach contributes to advancing green chemistry practices and to promoting sustainable solvent usage.