Edward Joseph Maginn

The vaporization enthalpy increases linearly with increasing alkyl chain length n on cation, but with different slopes before and after n ≥ 10. The reason is a competition between vdW and electrostatic contributions to vaporisation enthalpy.

Choline chloride-based deep eutectic solvents (DESs) are being investigated as redox flow battery electrolytes, where solvent viscosity plays a central role in governing ionic mobility, mass transport, and pumping efficiency. In this combined experimental and computational study, a series of DESs formed from choline chloride and butanediol isomer hydrogen bond donors (HBDs) is examined to probe how HBD molecular geometry affects liquid structure and dynamics while holding the molecular formula and hydroxyl group count constant. Density, viscosity, ionic conductivity, and ET(30) polarity are measured experimentally, while molecular dynamics simulations are used to quantify coordination numbers, hydrogen-bond lifetimes, self-diffusivities, dynamic heterogeneity, and local packing. Although the dominant first-shell interaction motifs are preserved across all isomers, systematic differences in hydrogen-bond dynamics, diol–diol connectivity, and spatial heterogeneity give rise to distinct transport behaviors, including substantial variations in viscosity and conductivity, that cannot be attributed to changes in local coordination structure alone. These results establish hydroxyl group arrangement as a key structural parameter governing DES transport properties and provide a controlled molecular framework for the rational design of choline chloride-based electrolytes.

The calculation of reliable ionic conductivities from molecular dynamics simulations is not a straightforward task, especially for strongly correlated systems, such as ionic liquids or highly concentrated electrolytes, where the Nernst–Einstein approach tends to fail. In this manuscript, we present the newly implemented conduct module for TRAVIS. It allows the calculation of the ionic conductivity using the Einstein–Helfand and Green–Kubo approaches, which explicitly include ionic correlations in their formalism. We provide a broad overview of accessible transport properties and compare methods and best practices for obtaining statistically reliable estimates of ionic conductivity and other physicochemical properties derived from electrolyte molecular dynamics simulations, including transport numbers and the inverse Haven ratio. To validate our implementation and demonstrate the conduct module's capabilities, we simulated the ionic liquid 1‐ethyl‐3‐methylimidazolium dicyanamide ([EMIm][DCA]) as well as the ether‐based electrolyte lithium bis(fluorosulfonyl)imide in ethylene glycol dimethyl ether (LiFSI/DME).

Noble gas behavior in molten alkali chloride salts is important for understanding gas retention, transport, and removal in high-temperature molten salt environments. Although previous molecular simulations have examined single-particle properties such as noble gas solubility and diffusion, the thermodynamics of early-stage gas aggregation in molten salts remain less well understood. In this work, we develop an ab initio-informed classical molecular dynamics framework to quantify stepwise noble gas aggregation in molten LiCl and KCl. Salt-specific gas-ion interaction parameters are obtained by combining quantum mechanical gas-ion interaction scans with ab initio molecular dynamics structural benchmarks. The resulting classical models are then used to compute potentials of mean force for two-body, three-body, and four-body association geometries of argon and xenon. Long-timescale simulations are also used to compute diffusion coefficients, providing a complementary dynamical perspective on gas behavior in each salt. The potentials of mean force show that argon exhibits a consistent tendency toward local association in both salts, with stronger stabilization in LiCl. Xenon displays a more salt-dependent response: KCl supports increasingly favorable xenon association across the sampled aggregation geometries, whereas LiCl shows weaker association and larger intermediate barriers. Diffusion coefficients indicate that both gases are more mobile in KCl than in LiCl, with argon diffusing slightly faster than xenon within a given salt. Overall, these results show that noble gas aggregation in molten salts cannot be predicted from gas size or gas-gas attraction alone, but instead depends on the coupling between gas identity and the salt-specific local environment.

Noble gas behavior in molten alkali chloride salts is important for understanding gas retention, transport, and removal in high-temperature molten salt environments. Although previous molecular simulations have examined single-particle properties such as noble gas solubility and diffusion, the thermodynamics of early-stage gas aggregation in molten salts remain less well understood. Here, we develop an ab initio-informed classical molecular dynamics framework for quantifying stepwise noble gas aggregation in molten LiCl and KCl. Salt-specific gas–ion interaction parameters are obtained by combining quantum mechanical gas–ion interaction scans with ab initio molecular dynamics structural benchmarks. The resulting classical models are then used to compute potentials of mean force (PMFs) for two-body, three-body, and four-body association geometries of argon and xenon. Longtimescale simulations are also used to compute diffusion coefficients, providing a complementary dynamical perspective on gas behavior in each salt. The PMFs show that argon exhibits a consistent tendency toward local association in both salts, with stronger stabilization in LiCl. Xenon displays a more salt-dependent response: KCl supports increasingly favorable Xe association across the sampled aggregation geometries, whereas LiCl shows weaker association and larger intermediate barriers. Diffusion coefficients indicate that both gases are more mobile in KCl than in LiCl, with argon diffusing slightly faster than xenon within a given salt. Overall, these results show that noble gas aggregation in molten salts cannot be predicted from gas size or gas–gas attraction alone, but instead depends on the coupling between gas identity and the salt-specific local environment.

Sigma profiles (SPs) are powerful molecular descriptors that combine the size-independence and continuity of many machine-learned descriptors with the physical interpretability of structural descriptors like molecular graphs or SMILES. Though SPs have existed for a long time within the context of thermodynamic models like COSMO-RS and COSMO-SAC, there does not seem to be a consensus in the literature on the effect of molecular conformations on SPs or on their performance as inputs to thermodynamic or machine learning (ML) models. This work performs a systematic study on the effect of conformations on SPs as an input to ML models by developing three different conformer generation heuristics and using the resulting SP datasets to train Gaussian Process (GP) models as an inexpensive surrogate ML model. All SPs are generated using the open- source tool OpenSPGen following the suggested guidelines for level of theory, tessellation, and post-processing (averaging radius). The study shows that conformations have a small but statistically significant effect on the ML prediction of three out of six considered target properties. Additionally, it shows that the effect of conformations is more significant for smaller molecules, and that the number of conformations included is more important than the heuristic of conformer generation. The final recommendation of this work is to start by generating five random conformers, examine how the ML performance for the target property of interest changes with increasing the number of random conformers, then decide if the increase in performance from one to three to five conformers justifies generating more conformations and more SPs. Design, System, Application Sigma profiles (SPs) are a powerful molecular descriptor for physics-informed machine learning (ML) for molecular design applications because they uniquely combine physical significance and human-interpretability with continuity and size-independence. However, using SPs in ML applications requires guidelines for open-source SP generation at scale. This work develops guidelines for conformer generation and selection to yield the highest-performing SPs for ML applications. A systematic study on the effect of conformations on SPs and their performance as an ML descriptor was performed. The resulting guidelines were implemented as a default option in the open-source SP generation package OpenSPGen allowing consistent treatment of

Transferable hydrofluorocarbon force fields that accurately predict thermophysical property data can be rapidly postulated and parameterized with Gaussian process models.