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Ionic liquids (ILs) are a unique class of solvents with potential applications in advanced separation technologies relevant to the nuclear industry. ILs are salts with low melting points and a wide range of tunable physical properties, such as viscosity, hydrophobiciy, conductivity, and liquidus range. ILs have negligible vapor pressure, are often non-flammable, and can have high thermal stability and a wide electrochemical window, making them attractive for use in separations processes relevant to the nuclear industry. Metal salts generally have a low solubility in ILs; however, by incorporating new functional groups into the IL cation or anion that promote complexation with the metal, the solubility can be greatly increased. One such task-specific ionic liquid (TSIL) is 1-carboxy-N, N, N-trimethylglycine bis(trifluoromethylsulfonyl)imide ([Hbet][Tf2N]) [Nockemann et al., J. Phys. Chem. B 110, 20978-20992 (2006)]. Water, which is detrimental for electrochemical separations, is a common impurity in ILs and can coordinate with actinyl cations, particularly in ILs containing only weakly coordinating components. Understanding the behavior of actinides in TSIL/water mixtures on a molecular level is vital for designing improved separations processes. Classical molecular dynamics simulations of uranyl(VI) and plutonyl(VI) in 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][Tf2N]) with deprotonated Hbet (betaine) and water have been performed to understand the coordination and dynamics of the actinyl cations. We find that betaine is a much stronger ligand than water and prefers to coordinate the metal in a bidentate manner. Potential of mean force simulations yield a relative free energy for betaine coordination of approximately -120 to -90 kJ/mol in mixtures with water. As the amount of betaine coordinated to the actinide increases, the diffusion coefficient of the actinyl cation decreases. Moreover, the betaine ligand is able to bridge between two metal centers, resulting in dimeric complexes with actinide-actinide distances of ∼5 Å. Potential of mean force simulations show that these structures are stable, with relative free energies of up to -40 kJ/mol. The crystal structure for [(UO2)2(bet)6(H2O)2][Tf2N]4 shows that the betaine bridges between two uranium atoms to form dimeric complexes similar to those found in our simulations [Nockemann et al. Inorg. Chem. 49, 3351-33601 (2010)]., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Hydrofluorocarbons are a class of fluorinated molecules used extensively in residential and industrial refrigeration systems. This study examines the potential of using adsorption processes with the silicalite-1 zeolite to separate a mixture of difluoromethane (CH2F2, HFC-32) and pentafluoroethane (CF3CF2H, HFC-125) at various concentrations. Pure adsorption data were measured using a XEMIS gravimetric microbalance, whereas binary data were determined using the Integral Mass Balance method. Grand canonical Monte Carlo molecular simulations were performed with the Cassandra package. We found that the results from molecular simulations are in satisfactory agreement with experimental loading measurements. Moreover, we show that ideal adsorbed solution theory could not quantitatively match the experimental or computational measurements of binary adsorption or selectivity. Molecular simulations show that refrigerant molecules do not have a uniform distribution in the zeolite framework., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available., Competing Interests: Competing interests statement:The authors declare no competing interest.

In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro-Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density-temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

This review discusses the research being performed on ionic liquids for the separation of fluorocarbon refrigerant mixtures. Fluorocarbon refrigerants, invented in 1928 by Thomas Midgley Jr., are a unique class of working fluids that are used in a variety of applications including refrigeration. Fluorocarbon refrigerants can be categorized into four generations: chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, and hydrofluoroolefins. Each generation of refrigerants solved a key problem from the previous generation; however, each new generation has relied on more complex mixtures that are often zeotropic, near azeotropic, or azeotropic. The complexity of the refrigerants used and the fact that many refrigerants form azeotropes when mixed makes handling the refrigerants at end of life extremely difficult. Today, less than 3% of refrigerants that enter the market are recycled. This is due to a lack of technology in the refrigerant reclaim market that would allow for these complex, azeotropic refrigerant mixtures to be separated into their components in order to be effectively reused, recycled, and if needed repurposed. As the market for recovering and reclaiming refrigerants continues to grow, there is a strong need for separation technology. Ionic liquids show promise for separating azeotropic refrigerant mixtures as an entrainer in extractive distillation process. Ionic liquids have been investigated with refrigerants for this application since the early 2000s. This review will provide a comprehensive summary of the physical property measurements, equations of state modeling, molecular simulations, separation techniques, and unique materials unitizing ionic liquids for the development of an ionic-liquid-based separation process for azeotropic refrigerant mixtures.

Ionic liquids (ILs) have been used in many applications, including gas separations, electrochemistry, lubrication, and catalysis. Understanding how the different properties of ILs are related to their chemical structure and composition is crucial for these applications. Experimental investigations often provide limited insights and can be tedious in exploring a range of state points. Therefore, molecular simulations have emerged as a powerful tool that not only offers a microscopic perspective but also enables rapid screening and prediction of physical properties. The accuracy of these predictions, however, depends on the quality of the intermolecular potentials (force fields) used. The widely used classical fixed charge models, such as GAFF, OPLS, and CL&P, are popular due to their simplicity and computational efficiency. However, it has been shown that the use of integer charges with these classical models leads to sluggish dynamics. The use of scaled charge models can improve the dynamics, but these mean-field approaches are unable to account for polarization effects explicitly. Several different approaches have been proposed to include polarizability in IL force fields. In this work, we follow the protocol of the CL&Pol model to develop a Drude oscillator model based on the GAFF force field (Goloviznina, K., et al. J. Chem. Theory Comput. 2019 , 15 , 5858). We compare the performance of the model for eight imidazolium- and pyrrolidinium-based ILs against that of other models. We find that the new model provides reasonable estimations of density, self-diffusivity, and structural properties for these ILs and suggests a relatively simple way of extending the general GAFF model to more ILs.

Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.

Aqueous electrolytes composed of 0.1 M zinc bis(trifluoromethylsulfonyl)imide (Zn(TFSI) 2 ) and acetonitrile (ACN) were studied using combined experimental and simulation techniques. The electrolyte was found to be electrochemically stable when the ACN V% is higher than 74.4. In addition, it was found that the ionic conductivity of the mixed solvent electrolytes changes as a function of ACN composition, and a maximum was observed at 91.7 V% of ACN although the salt concentration is the same. This behavior was qualitatively reproduced by molecular dynamics (MD) simulations. Detailed analyses based on experiments and MD simulations show that at high ACN composition the water network existing in the high water composition solutions breaks. As a result, the screening effect of the solvent weakens and the correlation among ions increases, which causes a decrease in ionic conductivity at high ACN V%. This study provides a fundamental understanding of this complex mixed solvent electrolyte system.

Aqueous zinc-ion batteries (ZIBs) employing zinc metal anodes are gaining traction as batteries for moderate to long duration energy storage at scale. However, corrosion of the zinc metal anode through reaction with water limits battery efficiency. Much research in the past few years has focused on additives that decrease hydrogen evolution, but the precise mechanisms by which this takes place are often understudied and remain unclear. In this work, we study the role of an acetonitrile antisolvent additive in improving the performance of aqueous ZnSO 4 electrolytes using experimental and computational techniques. We demonstrate that acetonitrile actively modifies the interfacial chemistry during Zn metal plating, which results in improved performance of acetonitrile-containing electrolytes. Collectively, this work demonstrates the effectiveness of solvent additive systems in battery performance and durability and provides a new framework for future efforts to optimize ion transport and performance in ZIBs., Competing Interests: The authors declare no competing financial interest., (© 2023 UChicago Argonne, LLC, Operator of Argonne National Laboratory. Published by American Chemical Society.)

The intermolecular interaction energies, including hydrogen bonds (H-bonds), of clusters of the ionic liquid ethylammonium nitrate (EAN) and 1-amino-1,2,3-triazole (1-AT) based deep eutectic propellants (DeEP) are examined. 1-AT is introduced as a neutral hydrogen bond donor (HBD) to EAN in order to form a eutectic mixture. The effective fragment potential (EFP) is used to examine the bonding interactions in the DeEP clusters. The resolution of the Identity (RI) approximated second order Møller-Plesset perturbation theory (RI-MP2) and coupled cluster theory (RI-CCSD(T)) are used to validate the EFP results. The EFP method predicts that there are significant polarization and charge transfer effects in the EAN:1-AT complexes, along with Coulombic, dispersion and exchange repulsion interactions. The EFP interaction energies are in good agreement with the RI-MP2 and RI-CCSD(T) results. The quasi-atomic orbital (QUAO) bonding and kinetic bond order (KBO) analyses are additionally used to develop a conceptual and semi-quantitative understanding of the H-bonding interactions as a function of the size of the system. The QUAO and KBO analyses suggest that the H-bonds in the examined clusters follow the characteristic hydrogen bonding three-center four electron interactions. The strongest H-bonding interactions between the (EAN) 1 :(1-AT) n and (EAN) 2 :(1-AT) n ( n = 1-5) complexes are observed internally within EAN; that is, between the ethylammonium cation [EA] + and the nitrate anion ([NO 3 ] - ). The weakest H-bonding interactions occur between [NO 3 ] - and 1-AT. Consequently, the average strengths of the H-bonds within a given (EAN) x :(1-AT) n complex decrease as more 1-AT molecules are introduced into the EAN monomer and EAN dimer. The QUAO bonding analysis suggests that 1-AT in (EAN) x :(1-AT) n can act as both a HBD and a hydrogen bond acceptor simultaneously. It is observed that two 1-AT molecules can form H-bonds to each other. Although the KBOs that correspond to H-bonding interactions in [EA] + :1-AT, [NO 3 ] - :1-AT and between two 1-AT molecules are weaker than the H-bonds in EAN, those weak H-bond networks with 1-AT could be important to form a stable DeEP.

Alchemical free energy calculations via molecular dynamics have been applied to obtain thermodynamic properties related to solid-liquid equilibrium conditions, such as melting points. In recent years, the pseudo-supercritical path (PSCP) method has proved to be an important approach to melting point prediction due to its flexibility and applicability. In the present work, we propose improvements to the PSCP alchemical cycle to make it more compact and efficient through a concerted evaluation of different potential energies. The multistate Bennett acceptance ratio (MBAR) estimator was applied at all stages of the new cycle to provide greater accuracy and uniformity, which is essential concerning uncertainty calculations. In particular, for the multistate expansion stage from solid to liquid, we employed the MBAR estimator with a reduced energy function that allows affine transformations of coordinates. Free energy and mean derivative profiles were calculated at different cycle stages for argon, triazole, propenal, and the ionic liquid 1-ethyl-3-methyl-imidazolium hexafluorophosphate. Comparisons showed a better performance of the proposed method than the original PSCP cycle for systems with higher complexity, especially the ionic liquid. A detailed study of the expansion stage revealed that remapping the centers of mass of the molecules or ions is preferable to remapping the coordinates of each atom, yielding better overlap between adjacent states and improving the accuracy of the methodology., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

Hydrofluorocarbon (HFC) refrigerants with zero ozone-depleting potential have replaced chlorofluorocarbons and are now ubiquitous. However, some HFCs have high global warming potential, which has led to calls by governments to phase out these HFCs. Technologies to recycle and repurpose these HFCs need to be developed. Therefore, thermophysical properties of HFCs are needed over a wide range of conditions. Molecular simulations can help understand and predict the thermophysical properties of HFCs. The prediction capability of a molecular simulation is directly tied to the accuracy of the force field. In this work, we applied and refined a machine learning-based workflow to optimize the Lennard-Jones parameters of classical HFC force fields for HFC-143a (CF 3 CH 3 ), HFC-134a (CH 2 FCF 3 ), R-50 (CH 4 ), R-170 (C 2 H 6 ), and R-14 (CF 4 ). Our workflow involves liquid density iterations with molecular dynamics simulations and vapor-liquid equilibrium (VLE) iterations with Gibbs ensemble Monte Carlo simulations. Support vector machine classifiers and Gaussian process surrogate models save months of simulation time and can efficiently select optimal parameters from half a million distinct parameter sets. Excellent agreement as evidenced by low mean absolute percent errors (MAPEs) of simulated liquid density (ranging from 0.3% to 3.4%), vapor density (ranging from 1.4% to 2.6%), vapor pressure (ranging from 1.3% to 2.8%), and enthalpy of vaporization (ranging from 0.5% to 2.7%) relative to experiments was obtained for the recommended parameter set of each refrigerant. The performance of each new parameter set was superior or similar to the best force field in the literature.

Ionic liquids (ILs) have shown promise for applications that leverage differential gas solubility in an IL solvent, e.g., gas separations. Although most available literature provides Henry's law constants, the ability to efficiently estimate full isotherms is important for engineering design calculations. Molecular simulation can be used as a tool to predict full isotherms of gas in ILs. However, particle insertions or deletions in a charge-dense IL medium and the sluggish conformational dynamics of ILs present two sampling challenges for these systems. We therefore devised a method that uses Hamiltonian replica exchange (HREX) molecular dynamics (MD) combined with alchemical free energy calculations to compute full solubility isotherms of two different hydrofluorocarbons (HFCs) in imidazolium-based IL binary mixtures. This workflow is significantly faster than the Gibbs ensemble Monte Carlo (GEMC) simulations which fail to deal with the slow conformational relaxation caused by the sluggish dynamics of ILs. Multiple free energy estimators, including thermodynamic integration, free energy perturbation, and multistate Bennett acceptance ratio method, provided consistent results. Overall, the simulated Henry's law constant, isotherm curvature, and solubility trends match experimental results reasonably well. We close by calculating the full solubility isotherms of two HFCs in IL mixtures that have not been reported in the literature, demonstrating the potential of this method to be used for solubility prediction and setting the stage for future computational screening studies that search for the "best" IL to separate azeotropic HFC mixtures.

We present the results of molecular dynamics simulations of the ionic liquid (IL) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C 2 C 1 im][NTf 2 ] in the presence of external electric fields (EEFs) of varying strengths to understand the effects of EEFs on the glass transition temperature T g . We compute T g with an automated and objective method and observe a depression in T g when cooling the IL within an EEF above a critical strength. The effect is reversible, and glasses prepared with EEFs recover their original zero-field T g when heated. By examining the dynamics and structure of the liquid phase, we find that the EEF lowers the activation energy for diffusion, reducing the energetic barrier for movement and consequently T g . We show that the effect can be leveraged to drive an electrified nonvapor compression refrigeration cycle.

An ab initio quantum chemical approach for the modeling of propellant degradation is presented. Using state-of-the-art bonding analysis techniques and composite methods, a series of potential degradation reactions are devised for a sample hydroxyl-terminated-polybutadiene (HTPB) type solid fuel. By applying thermochemical procedures and isodesmic reactions, accurate thermochemical quantities are obtained using a modified G3 composite method based on the resolution of the identity. The calculated heats of formation for the different structures produced presents an ∼2 kcal/mol average error when compared against experimental values.

The physical properties of four ionic liquids (ILs), including 1- n -butyl-3-methylimidazolium tetrafluoroborate ([C 4 C 1 im][BF 4 ]), 1- n -butyl-3-methylimidazolium hexafluorophosphate ([C 4 C 1 im][PF 6 ]), 1- n -butyl-3-methylimidazolium thiocyanate ([C 4 C 1 im][SCN]), and 1- n -hexyl-3-methylimidazolium chloride ([C 6 C 1 im][Cl]), and their mixtures with hydrofluorocarbon (HFC) gases HFC-32 (CH 2 F 2 ), HFC-125 (CHF 2 CF 3 ), and HFC-410A, a 50/50 wt % mixture of HFC-32 and HFC-125, were studied using molecular dynamics (MD) simulation. Experiments were conducted to measure the density, self-diffusivity, and shear viscosity of HFC/[C 4 C 1 im][BF 4 ] system. Extensive analyses were carried out to understand the effect of IL structure on various properties of the HFC/IL mixtures. Density, diffusivity, and viscosity of the pure ILs were calculated and compared with experimental values. The good agreement between computed and experimental results suggests that the applied force fields are reliable. The calculated center of mass (COM) radial distribution functions (RDFs), partial RDFs, spatial distribution functions (SDFs), and coordination numbers (CNs) provide a sense of how the distribution of HFC changes in the liquid mixtures with IL structure. Detailed analysis reveals that selectivity toward HFC-32 and HFC-125 depends on both cation and anion. The molecular insight provided in the current work will help the design of optimal ILs for the separation of azeotropic HFC mixtures.

Experimental measurements and classical molecular dynamics (MD) simulations were carried out to study electrolytes containing CuCl 2 and CuCl salts in mixtures of choline chloride (ChCl) and ethylene glycol (EG). The study focused on the concentration of 100 mM of both CuCl 2 and CuCl with the ratio of ChCl/EG varied from 1:2, 1:3, 1:4, to 1:5. It was found that the Cu 2+ and Cu + have different solvation environments in their first solvation shell. Cu 2+ is coordinated by both Cl - anions and EG molecules, whereas Cu + is only solvated by EG. However, both Cu 2+ and Cu + show strong interactions with their second solvation shells, which include both Cl - anions and EG molecules. Considering both the first and second solvation shells, the concentrations of Cu 2+ and Cu + that have various coordination numbers in each solution were calculated and were found to correlate qualitatively with the exchange current density trends reported in previous experiments of Cu 2+ reduction to Cu + . This finding makes a connection between atomic solvation structure observed in MD simulations and redox reaction kinetics measured in electrochemical experiments, thus revealing the significance of the solvation environment of reduced and oxidized species for electrokinetics in deep eutectic solvents.

Superconcentrated electrolytes have emerged as a promising class of materials for energy storage devices, with evidence that high voltage performance is possible even with water as the solvent. Here, we study the changes in the water hydrogen bonding network induced by the dissolution of lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) in concentrations ranging from the dilute to the superconcentrated regimes. Using time-resolved two-dimensional infrared spectroscopy, we observe the progressive disruption of the water-water hydrogen bond network and the appearance of isolated water molecules interacting only with ions, which can be identified and spectroscopically isolated through the intermolecular cross-peaks between the water and the TFSI - ions. Analyzing the vibrational relaxation of excitations of the H 2 O stretching mode, we observe a transition in the dominant relaxation path as the bulk-like water vanishes and is replaced by ion-solvation water with the rapid single-step relaxation of delocalized stretching vibrations into the low frequency modes being replaced by multistep relaxation through the intramolecular H 2 O bend and into the TFSI - high frequency modes prior to relaxing to the low frequency structural degrees of freedom. These results definitively demonstrate the absence of vibrationally bulk-like water in the presence of high concentrations of LiTFSI and especially in the superconcentrated regime, while additionally revealing aspects of the water hydrogen bond network that have been difficult to discern from the vibrational spectroscopy of the neat liquid.

Understanding the mechanisms of charge transport in batteries is important for the rational design of new electrolyte formulations. Persistent questions about ion transport mechanisms in battery electrolytes are often framed in terms of vehicular diffusion by persistent ion-solvent complexes versus structural diffusion through the breaking and reformation of ion-solvent contacts, i.e., solvent exchange events. Ultrafast two-dimensional (2D) IR spectroscopy can probe exchange processes directly via the evolution of the cross-peaks on picosecond time scales. However, vibrational energy transfer in the absence of solvent exchange gives rise to the same spectral signatures, hiding the desired processes. We employ 2D IR on solvent resonances of a mixture of acetonitrile isotopologues to differentiate chemical exchange and energy-transfer dynamics in a comprehensive series of Li + , Mg 2+ , Zn 2+ , Ca 2+ , and Ba 2+ bis(trifluoromethylsulfonyl)imide electrolytes from the dilute to the superconcentrated regime. No exchange phenomena occur within at least 100 ps, regardless of the ion identity, salt concentration, and presence of water. All of the observed spectral dynamics originate from the intermolecular energy transfer. These results place the lower experimental boundary on the ion-solvent residence times to several hundred picoseconds, much slower than previously suggested. With the help of MD simulations and conductivity measurements on the Li + and Zn 2+ systems, we discuss these results as a continuum of vehicular and structural modalities that vary with concentration and emphasize the importance of collective electrolyte motions to ion transport. These results hold broadly applicable to many battery-relevant ions and solvents.

We report a systematic diffraction study of two "water-in-salt" electrolytes and a "water-in-bisalt" electrolyte combining high-energy X-ray diffraction (HEXRD) with polarized and unpolarized neutron diffraction (ND) on both H 2 O and D 2 O solutions. The measurements provide three independent combinations of correlations between the different pairs of atom types that reveal the short- and intermediate-range order in considerable detail. The ND interference functions show pronounced peaks around a scattering vector Q ∼ 0.5 Å -1 that change dramatically with composition, indicating significant rearrangements of the water network on a length scale around 12 Å. The experimental results are compared with two sets of Molecular Dynamics (MD) simulations, one including polarization effects and the other based on a non-polarizable force field. The two simulations reproduce the general shapes of the experimental structure factors and their changes with concentration, but differ in many detailed respects, suggesting ways in which their force fields might be modified to better represent the actual systems.

This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide range of physicochemical properties. The architectures developed are then exploited to include temperature as an additional feature.

Deep eutectic solvents (DESs) are an emerging class of non-aqueous solvents that are potentially scalable, easy to prepare and functionalize for many applications ranging from biomass processing to energy storage technologies. Predictive understanding of the fundamental correlations between local structure and macroscopic properties is needed to exploit the large design space and tunability of DESs for specific applications. Here, we employ a range of computational and experimental techniques that span length-scales from molecular to macroscopic and timescales from picoseconds to seconds to study the evolution of structure and dynamics in model DESs, namely Glyceline and Ethaline, starting from the parent compounds. We show that systematic addition of choline chloride leads to microscopic heterogeneities that alter the primary structural relaxation in glycerol and ethylene glycol and result in new dynamic modes that are strongly correlated to the macroscopic properties of the DES formed., (© 2022. The Author(s).)

The transport properties of water-in-salt lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) aqueous electrolytes were studied using classical molecular dynamics (MD) simulations. At high salt concentrations of 20 m, the calculated viscosity, self-diffusion coefficients, ionic conductivity, the inverse Haven ratio, and the Li + apparent transference number all agree with previous experimental results quantitatively. Furthermore, analyses show that the high apparent transference number for Li + is due to the fact that the dynamics of TFSI - decrease more quickly with increasing salt concentration than the dynamics of Li + ions due to the formation of a TFSI - network. In addition, it was shown that the conduction of Li + ions through the highly concentrated electrolyte occurs mainly via a hopping mechanism instead of a vehicular mechanism hypothesized in earlier studies of this system.

We describe and validate a free-energy-based method for computing the liquidus for binary solid-liquid phase diagrams in molecular simulations of monatomic salts. The method is demonstrated by calculating the liquidus for LiCl-KCl and MgCl 2 -KCl salt mixtures with the polarizable ion model (PIM). The free-energy-based method is cross-validated with direct coexistence simulations. Both techniques show excellent agreement with one another. Though the predictions of the PIM disagree with experiments, we use our free-energy-based approach to decouple the contributions of liquid mixture nonidealities and pure component solid-liquid equilibrium to the phase diagram. In both mixtures, the PIM accurately reproduces the liquid phase nonidealities but fails to predict the liquidus because it does not accurately predict the pure component melting temperature of LiCl or MgCl 2 .

Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.

We introduce a new Python interface for the Cassandra Monte Carlo software, molecular simulation design framework (MoSDeF) Cassandra. MoSDeF Cassandra provides a simplified user interface, offers broader interoperability with other molecular simulation codes, enables the construction of programmatic and reproducible molecular simulation workflows, and builds the infrastructure necessary for high-throughput Monte Carlo studies. Many of the capabilities of MoSDeF Cassandra are enabled via tight integration with MoSDeF. We discuss the motivation and design of MoSDeF Cassandra and proceed to demonstrate both simple use-cases and more complex workflows, including adsorption in porous media and a combined molecular dynamics - Monte Carlo workflow for computing lateral diffusivity in graphene slit pores. The examples presented herein demonstrate how even relatively complex simulation workflows can be reduced to, at most, a few files of Python code that can be version-controlled and shared with other researchers. We believe this paradigm will enable more rapid research advances and represents the future of molecular simulations., (© 2021 Wiley Periodicals LLC.)

Aqueous rechargeable zinc metal batteries promise attractive advantages including safety, high volumetric energy density, and low cost; however, such benefits cannot be unlocked unless Zn reversibility meets stringent commercial viability. Herein, we report remarkable improvements on Zn reversibility in aqueous electrolytes when phosphonium-based cations are used to reshape interfacial structures and interphasial chemistries, particularly when their ligands contain an ether linkage. This novel aqueous electrolyte supports unprecedented Zn reversibility by showing dendrite-free Zn plating/stripping for over 6400 h at 0.5 mA cm -2 , or over 280 h at 2.5 mA cm -2 , with coulombic efficiency above 99 % even with 20 % Zn utilization per cycle. Excellent full cell performance is demonstrated with Na 2 V 6 O 16 ⋅1.63 H 2 O cathode, which cycles for 2000 times at 300 mA g -1 . The microscopic characterization and modeling identify the mechanism of unique interphase chemistry from phosphonium and its functionalities as the key factors responsible for dictating reversible Zn chemistry., (© 2021 Wiley-VCH GmbH.)

The concept of water-in-salt electrolytes was introduced recently, and these systems have been successfully applied to yield extended operation voltage and hence significantly improved energy density in aqueous Li-ion batteries. In the present work, results of X-ray scattering and Fourier-transform infrared spectra measurements over a wide range of temperatures and salt concentrations are reported for the LiTFSI (lithium bis(trifluoromethane sulfonyl)imide)-based water-in-salt electrolyte. Classical molecular dynamics simulations are validated against the experiments and used to gain additional information about the electrolyte structure. Based on our analyses, a new model for the liquid structure is proposed. Specifically, we demonstrate that at the highest LiTFSI concentration of 20 m the water network is disrupted, and the majority of water molecules exist in the form of isolated monomers, clusters, or small aggregates with chain-like configurations. On the other hand, TFSI - anions are connected to each other and form a network. This description is fundamentally different from those proposed in earlier studies of this system.

Deep eutectic solvents (DESs) are an emerging class of mixtures characterized by significant depressions in melting points compared to those of the neat constituent components. These materials are promising for applications as inexpensive "designer" solvents exhibiting a host of tunable physicochemical properties. A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure-property relationships in this class of solvents. Complex hydrogen bonding is postulated as the root cause of their melting point depressions and physicochemical properties; to understand these hydrogen bonded networks, it is imperative to study these systems as dynamic entities using both simulations and experiments. This review emphasizes recent research efforts in order to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding of DESs. It covers recent developments in DES research, frames outstanding scientific questions, and identifies promising research thrusts aligned with the advancement of the field toward predictive models and fundamental understanding of these solvents.

Results from extensive molecular dynamics simulations of molten LiCl, NaCl, KCl, and RbCl over a wide range of temperatures are reported. Comparison is made between the "Polarizable Ion Model" (PIM) and the non-polarizable "Rigid Ion Model" (RIM). Densities, self-diffusivities, shear viscosities, ionic conductivities, and thermal conductivities are computed and compared with experimental data. In addition, radial distribution functions are computed from ab initio molecular dynamics simulations and compared with the two sets of classical simulations as well as experimental data. The two classical models perform reasonably well at capturing structural and dynamic properties of the four molten alkali chlorides, both qualitatively and often quantitatively. With the singular exception of liquid density, for which the PIM is more accurate than the RIM, there are few clear trends to suggest that one model is more accurate than the other for the four alkali halide systems studied here.

Molten salts are of great interest as alternative solvents, electrolytes, and heat transfer fluids in many emerging technologies. The macroscopic properties of molten salts are ultimately controlled by their structure and ion dynamics at the microscopic level and it is therefore vital to develop an understanding of these at the atomistic scale. Herein, we present high-energy X-ray scattering experiments combined with classical and ab initio molecular dynamics simulations to elucidate structural and dynamical correlations across the family of alkali-chlorides. Computed structure functions and transport properties are in reasonably good agreement with experiments providing confidence in our analysis of microscopic properties based on simulations. For these systems, we also survey different rate theory models of anion exchange dynamics in order to gain a more sophisticated understanding of the short-time correlations that are likely to influence transport properties such as conductivity. The anion exchange process occurs on the picoseconds time scale at 1100 K and the rate increases in the order KCl < NaCl < LiCl, which is in stark contrast to the ion pair dissociation trend in aqueous solutions. Consistent with the trend we observe for conductivity, the cationic size/mass, as well as other factors specific to each type of rate theory, appear to play important roles in the anion exchange rate trend.

There is a long history of models that to different extents reproduce structural and dynamical properties of high-temperature molten salts. Whereas rigid ion models can work fairly well for some of the monovalent salts, polarizability is fundamentally important when small divalent or multivalent cations are combined with significantly polarizable anions such as Cl - to form networked liquids that display a first sharp diffraction peak. There are excellent polarizable ion models (PIMs) for these systems, but there has been little success with the less expensive Core-Shell type models, which are often described as unwieldy or difficult to fit. In this article, we present the Sharma-Emerson-Margulis (SEM)-Drude model for MgCl 2 /KCl mixtures that with the same ingredients used in the latest and most accurate PIM models overcome the aforementioned obstacles at significantly less computational cost; structural and dynamical properties are for all practical purposes very similar to what we obtain from the PIM but typical simulations can be more than 30 times faster. This has allowed us not only to expand our recent studies on the temperature and composition dependence of intermediate range order in MgCl 2 /KCl mixtures but also to access transport properties that were simply too costly to properly sample in our recently published studies.

The low melting point of room temperature ionic liquids is usually explained in terms of the presence of bulky, low-symmetry, and flexible ions, with the first two factors related to the lattice energy while an entropic effect is attributed to the latter. By means of molecular dynamics simulations, the melting points of 1-ethyl-3-methyl-imidazolium hexafluorophosphate and 1-decyl-3-methyl-imidazolium hexafluorophosphate were determined, and the effect of the molecular flexibility over the melting point was explicitly computed by restraining the rotation of dihedral angles in both the solid and the liquid phases. The rotational flexibility over the bond between the ring and the alkyl chain affects the relative ordering of the anions around the cations and results in substantial effects over both the enthalpy and the entropy of melting. For the other dihedral angles of the alkyl group, the contributions are predominantly entropic and an alternating behavior was found. The flexibility of some dihedral angles has negligible effects on the melting point, while others can lead to differences in the melting point as large as 20 K. This alternating behavior is rationalized by the different probabilities of conformation defects in the crystal.

Lithium-air batteries have emerged as an interesting alternative for advanced energy storage devices. The complexity of such systems imposes great challenges. One of them resides in the selection of the lithium salt/solvent pair. Many electrolyte properties affect the operation of the batteries. Among these, the transport properties and structural features have a special place. Via molecular dynamics simulations, we have calculated solution viscosity, ionic diffusivities and conductivities, as well as structural information, for two different salts in dimethyl sulfoxide (DMSO): lithium hexafluorophosphate - LiPF6, and lithium pyrrolide - LiPyr, at different temperatures and salt molalities. We show that, despite similar ionic transport properties, Li+ solvation in the different salts is significantly different. Therefore, solutions with different solvation properties, which impact the overall battery performance, might present analogous ionic dynamics.

Accurate molecular models of pure alkali halides are a prerequisite for developing transferable models of molten salts that can predict the properties of complex salt mixtures, such as those including dissolved actinide species and metal ions. Predicting the melting point of a substance represents a rigorous test of model quality. To this end, we compute the melting points of the alkali chlorides for a popular non-polarizable and polarizable model. Neither model yields more accurate predictions of the melting points across the entire family of alkali chlorides. Further calculations suggest that this may be because neither model simultaneously represents both the solid and liquid phases with sufficient accuracy across all four alkali chlorides. We find that the deviation from experiment in the model enthalpy of melting may be a good indicator of the deviation from experiment in the model melting temperature. Since the enthalpy of melting is easier to calculate in simulation than melting temperature, it may be a useful quantity to target when developing new force fields for molten salts.

A range of techniques including physical property measurements, neutron scattering experiments, ab initio molecular dynamics, and classical molecular dynamics simulations are used to probe the structural, thermodynamic, and transport properties of a deep eutectic solvent comprised of a 1:2 molar ratio of choline chloride and ethylene glycol. This mixture, known as Ethaline, has many desirable properties for use in a range of applications, and therefore, understanding its liquid structure and transport properties is of interest. Simulation results are able to capture experimental densities, diffusivities, viscosities, and structure factors extremely well. The solvation environment is dynamic and dominated by different hydrogen bonding interactions. Dynamic heterogeneities resulting from hydrogen bonding interactions are quantified. Rotational dynamics of molecular dipole moments of choline and ethylene glycol are computed and found to exhibit a fast and slow mode.

Actinyl ions can self-assemble in aqueous solution to form closed cage clusters ranging from 1.5 to 4.0 nm in diameter. The self-assembly, stability, and behavior of the nanoclusters depend on the nature of the aqueous environment, such as the pH and cations present. In this work, a classical force field for [(UO2)20(O2)30]20- (U20) peroxide nanoclusters in aqueous solution was developed from quantum-mechanical calculations. Using molecular dynamics simulations, the preferred binding sites of six cations (Li+, Na+, K+, Rb+, Cs+, and Ca2+) to the nanocluster were determined. Replica exchange molecular dynamics was used to equilibrate the structure and determine the equilibrium distribution of cations and water with respect to the nanocluster cage. In addition, the free energy barriers associated with cations entering the cluster were computed. Finally, the association of two cages was investigated by computing the free energy as a function of intercage distance. The free energy profiles reveal that the nanoclusters prefer to be associated when neutralized with divalent cations, but do not associate when neutralized with monovalent cations. This could explain the formation of tertiary structures observed experimentally.