Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations.

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.

Ionic Liquids for the Separation of Fluorocarbon Refrigerant Mixtures.

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.

GAFF-Based Polarizable Force Field Development and Validation for Ionic Liquids.

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.

Effect of Antisolvent Additives in Aqueous Zinc Sulfate Electrolytes for Zinc Metal Anodes: The Case of Acetonitrile.

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 ZnSO4 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.

Understanding the Surprising Ionic Conductivity Maximum in Zn(TFSI)2 Water/Acetonitrile Mixture Electrolytes.

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.

Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction.

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.

Intermolecular interactions in clusters of ethylammonium nitrate and 1-amino-1,2,3-triazole.

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 ([NO3]-). The weakest H-bonding interactions occur between [NO3]- 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, [NO3]-: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.

Revisiting the pseudo-supercritical path method: An improved formulation for the alchemical calculation of solid-liquid coexistence.

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.

Machine Learning-Enabled Development of Accurate Force Fields for Refrigerants.

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 (CF3CH3), HFC-134a (CH2FCF3), R-50 (CH4), R-170 (C2H6), and R-14 (CF4). 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.

Molecular Dynamics Simulation of the Influence of External Electric Fields on the Glass Transition Temperature of the Ionic Liquid 1-Ethyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide.

We present the results of molecular dynamics simulations of the ionic liquid (IL) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C2C1im][NTf2] in the presence of external electric fields (EEFs) of varying strengths to understand the effects of EEFs on the glass transition temperature Tg. We compute Tg with an automated and objective method and observe a depression in Tg 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 Tg 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 Tg. We show that the effect can be leveraged to drive an electrified nonvapor compression refrigeration cycle.

Alchemical Free Energy and Hamiltonian Replica Exchange Molecular Dynamics to Compute Hydrofluorocarbon Isotherms in Imidazolium-Based Ionic Liquids.

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.

Quantum Chemical Modeling of Propellant Degradation.

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.

Structure and Dynamics of Hydrofluorocarbon/Ionic Liquid Mixtures: An Experimental and Molecular Dynamics Study.

The physical properties of four ionic liquids (ILs), including 1-n-butyl-3-methylimidazolium tetrafluoroborate ([C4C1im][BF4]), 1-n-butyl-3-methylimidazolium hexafluorophosphate ([C4C1im][PF6]), 1-n-butyl-3-methylimidazolium thiocyanate ([C4C1im][SCN]), and 1-n-hexyl-3-methylimidazolium chloride ([C6C1im][Cl]), and their mixtures with hydrofluorocarbon (HFC) gases HFC-32 (CH2F2), HFC-125 (CHF2CF3), 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/[C4C1im][BF4] 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.

Solvation Structure, Dynamics, and Charge Transfer Kinetics of Cu2+ and Cu+ in Choline Chloride Ethylene Glycol Electrolytes.

Experimental measurements and classical molecular dynamics (MD) simulations were carried out to study electrolytes containing CuCl2 and CuCl salts in mixtures of choline chloride (ChCl) and ethylene glycol (EG). The study focused on the concentration of 100 mM of both CuCl2 and CuCl with the ratio of ChCl/EG varied from 1:2, 1:3, 1:4, to 1:5. It was found that the Cu2+ and Cu+ have different solvation environments in their first solvation shell. Cu2+ is coordinated by both Cl- anions and EG molecules, whereas Cu+ is only solvated by EG. However, both Cu2+ 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 Cu2+ 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 Cu2+ 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.

From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two-Dimensional Infrared Spectroscopy.

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 H2O 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 H2O 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.

Exchange-Mediated Transport in Battery Electrolytes: Ultrafast or Ultraslow?

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+, Mg2+, Zn2+, Ca2+, and Ba2+ 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 Zn2+ 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.

Structure of water-in-salt and water-in-bisalt electrolytes.

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 H2O and D2O 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.

Sigma profiles in deep learning: towards a universal molecular descriptor.

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.