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.

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Understanding diffusion mechanisms in solid electrolytes is crucial for advancing solid-state battery technologies. This study investigates the role of structural disorder in Li7−xPS6−xBrx argyrodites using ab initio molecular dynamics, focusing on the correlation between key structural descriptors and Li-ion conductivity. Commonly suggested parameters, such as configurational entropy, bromide site occupancy, and bromine content, correlate with Li-ion diffusivity but do not consistently explain conductivity trends. We find that a uniform distribution of bromine and sulfur ions across the 4a and 4d sublattices is critical for achieving high conductivity by facilitating optimal lithium jump activation energies, anion-lithium distances, and charge distribution. Additionally, we introduce the ionic potential as a simple descriptor that predicts argyrodite conductivity by assessing the interaction strength between cations and anions. By analyzing the correlation between ionic potential and conductivity for a range of argyrodite compositions published over the past decade, we demonstrate its broad applicability. Minimizing and equalizing ionic potentials across both sublattices enhances conductivity by reducing the strength of anion-lithium interactions. Our analysis of local environments coordinating Li jumps reveals that balancing high and low-energy pathways is crucial for enabling macroscopic diffusion, supported by investigating percolating pathways. This study highlights the significance of the anionic framework in lithium mobility and informs the design of solid electrolytes for improved energy storage systems.@en

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.

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