Simulations of imidazolium ionic liquids: when does the cation charge distribution matter?

A DLPNO-CCSD(T) benchmarking study of intermolecular interactions of ionic liquids

The accuracy of correlation energy recovered by coupled cluster single-, double-, and perturbative triple-excitations, CCSD(T), has led to the method being considered the gold standard of computational chemistry. The application of CCSD(T) has been limited to medium-sized molecular systems due to its steep scaling with molecular size. The recent development of alternative domain-based local pair natural orbital coupled-cluster method, DLPNO-CCSD(T), has significantly broadened the range of chemical systems to which CCSD(T) level calculations can be applied. Condensed systems such as ionic liquids (ILs) have a large contribution from London dispersion forces of up to 150 kJ mol^-1 in large-scale clusters. Ionic liquids show appreciable charge transfer effects that result in the increased valence orbital delocalization over the entire ionic network, raising the question whether the application of methods based on localized orbitals is reliable for these semi-Coulombic materials. Here the performance of DLPNO-CCSD(T) is validated for the prediction of correlation interaction energies of two data sets incorporating single-ion pairs of protic and aprotic ILs. DLPNO-CCSD(T) produced results within chemical accuracy with tight parameter settings and a non-iterative treatment of triple excitations. To achieve spectroscopic accuracy of 1 kJ mol^-1 , especially for hydrogen-bonded ILs and those containing halides, the DLPNO settings had to be increased by two orders of magnitude and include the iterative treatment of triple excitations, resulting in a 2.5-fold increase in computational cost. Two new sets of parameters are put forward to produce the performance of DLPNO-CCSD(T) within chemical and spectroscopic accuracy.

A Differentiable Neural-Network Force Field for Ionic Liquids

We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Built on the basis of a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. Using ethylammonium nitrate as the test system, we obtain out-of-sample accuracies better than 2 meV atom-1 (<0.05 kcal mol-1) in the energies and 70 meV Å-1 in the forces. We show that encoding the element-specific density in the spherical Bessel descriptors is key to achieving this. Harnessing the information provided by the forces drastically reduces the amount of atomic configurations required to train a neural network force field based on atom-centered descriptors. We choose the Swish-1 activation function and discuss the role of this choice in keeping the neural network differentiable. Furthermore, the possibility of training on small data sets allows for an ensemble-learning approach to the detection of extrapolation. Finally, we find that a separate treatment of long-range interactions is not required to achieve a high-quality representation of the potential energy surface of these dense ionic systems.

Ionic liquid interface at an electrode: simulations of electrochemical properties using an asymmetric restricted primitive model

We use Monte Carlo simulations of a coarse-grained model to investigate structure and electrochemical behaviours at an electrode immersed in room temperature ionic liquids (RTILs). The simple RTIL model, which we denote the asymmetric restricted primitive model (ARPM), is composed of monovalent hard-sphere ions, all of the same size, in which the charge is asymmetrically placed. Not only the hard-sphere size (d), but also the charge displacement (b), is identical for all species, i.e. the monovalent RTIL ions are fully described by only two parameters (d, b). In earlier work, it was demonstrated that the ARPM can capture typical static RTIL properties in bulk solutions with remarkable accuracy. Here, we investigate its behaviour at an electrode surface. The electrode is assumed to be a perfect conductor and image charge methods are utilized to handle polarization effects. We find that the ARPM of the ionic liquid reproduces typical (static) electrochemical properties of RTILs. Our model predicts a declining differential capacitance with increasing temperature, which is expected from simple physical arguments. We also compare our ARPM, with the corresponding RPM description, at an elevated temperature (1000 K). We conclude that, even though ion pairing occurs in the ARPM system, reducing the concentration of 'free' ions, it is still better able to screen charge than a corresponding RPM melt. Finally, we evaluate the option to coarse-grain the model even further, by treating the fraction of the ions that form ion pairs implicitly, only through the contribution to the dielectric constant of the corresponding dipolar (ion pair) fluid. We conclude that this primitive representation of ion pairing is not able to reproduce the structures and differential capacitances of the system with explicit ion pairs. The main problem seems to be due to a limited dielectric screening in a layer near the electrode surface, resulting from a combination of orientational restrictions and a depleted dipole density.

Quantum Chemical Methods for the Prediction of Energetic, Physical, and Spectroscopic Properties of Ionic Liquids

The accurate prediction of physicochemical properties of condensed systems is a longstanding goal of theoretical (quantum) chemistry. Ionic liquids comprising entirely of ions provide a unique challenge in this respect due to the diverse chemical nature of available ions and the complex interplay of intermolecular interactions among them, thus resulting in the wide variability of physicochemical properties, such as thermodynamic, transport, and spectroscopic properties. It is well understood that intermolecular forces are directly linked to physicochemical properties of condensed systems, and therefore, an understanding of this relationship would greatly aid in the design and synthesis of functionalized materials with tailored properties for an application at hand. This review aims to give an overview of how electronic structure properties obtained from quantum chemical methods such as interaction/binding energy and its fundamental components, dipole moment, polarizability, and orbital energies, can help shed light on the energetic, physical, and spectroscopic properties of semi-Coulomb systems such as ionic liquids. Particular emphasis is given to the prediction of their thermodynamic, transport, spectroscopic, and solubilizing properties.

Towards large-scale, fully ab initio calculations of ionic liquids

Ionic liquids have attracted a substantial amount of interest as replacement of traditional electrolytes in high efficiency electrochemical devices for generation and storage of energy due to their superior physical and chemical properties, especially low volatility and high electrochemical stability. For enhanced performance of the electrochemical devices ionic liquids are required to be highly conductive and low viscous. Long-range Coulomb and short-range dispersion interactions between ions affect physical and chemical properties of ionic liquids in a very complex way, thus preventing direct correlations to the chemical structure. Considering a vast combination of available cations and anions that can be used to synthesize ionic liquids, development of predictive theoretical approaches that allow for accurate tailoring of their physical properties has become crucial to further enhance the performance of electrochemical devices such as lithium batteries, fuel and solar cells. This perspective article gives a thorough overview of current theoretical approaches applied for studying thermodynamic (melting point and enthalpy of vapourisation) and transport (conductivity and viscosity) properties of ionic liquids, emphasizing their reliability and limitations. Strategies for improving predictive power and versatility of existing theoretical approaches are also outlined.