Non-additive electronic polarizabilities of ionic liquids: Charge delocalization effects

Anion-Dependent Strength Scale of Interactions in Ionic Liquids from X-ray Photoelectron Spectroscopy, Ab Initio Molecular Dynamics, and Density Functional Theory

Using a combination of experiments and calculations, we have gained new insights into the nature of anion-cation interactions in ionic liquids (ILs). An X-ray photoelectron spectroscopy (XPS)-derived anion-dependent electrostatic interaction strength scale, determined using XPS core-level binding energies for IL cations, is presented here for 39 different anions, with at least 18 new anions included. Linear correlations of experimental XPS core-level binding energies for IL cations with (a) calculated core binding energies (ab initio molecular dynamics (AIMD) simulations were used to generate high-quality model IL structures followed by single-point density functional theory (DFT) to obtain calculated core binding energies), (b) experimental XPS core-level binding energies for IL anions, and (c) other anion-dependent interaction strength scales led to three main conclusions. First, the effect of different anions on the cation can be related to ground-state interactions. Second, the variations of anion-dependent interactions with the identity of the anion are best rationalized in terms of electrostatic interactions and not occupied valence state/unoccupied valence state interactions or polarizability-driven interactions. Therefore, the XPS-derived anion-dependent interaction strength scale can be explained using a simple electrostatic model based on electrostatic site potentials. Third, anion-probe interactions, irrespective of the identity of the probe, are primarily electrostatic, meaning that our electrostatic interaction strength scale captures some inherent, intrinsic property of anions independent of the probe used to measure the interaction strength scale.

Solvation Effects on Polarizability of Aromatic Fluids

Elucidating solvation effects on polarizability in condensed phases is important for the description of the optical and dielectric behavior of high-refractive-index molecular materials. We study these effects via the polarizability model combining electronic, solvation, and vibrational contributions. The method is applied to well-characterized highly polarizable liquid precursors: benzene, naphthalene, and phenanthrene. We find that the solvation and vibrational terms are of opposite signs and cancel almost exactly for benzene, but for naphthalene and phenanthrene, a 2.5 and 5.0% decrease relative to the equilibrium electronic polarizability of the respective monomer, α₁^e, is predicted, respectively. The increase in electronic polarizability affects interaction polarizability of all contacts, which is the main reason for the increasing importance of solvation contribution. The calculated refractive indices agree very well with experiment for all three systems.

Accurate and Interpretable Dipole Interaction Model-Based Machine Learning for Molecular Polarizability

Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although physical models or statistical machine learning models have been proposed, either a lack of accurate description of local chemical environments or demanding a large number of samples for training has limited their practical applications. In this study, we combine a physically inspired dipole interaction model and an accurate neural network method for predicting the polarizability tensors of molecules. With the local chemical environment precisely described and the requirement of rotational covariance naturally fulfilled, this hybrid model is proven to give an accurate molecular polarizability prediction, essentially reducing the number of training samples. The atomic polarizabilities are physically interpretable and transferable to larger molecules unseen in the training set. This promising method may find its wide range of applications, such as spectroscopic simulations and the construction of polarizable force fields.

Conductivity prediction model for ionic liquids using machine learning

Ionic liquids (ILs) are salts, composed of asymmetric cations and anions, typically existing as liquids at ambient temperatures. They have found widespread applications in energy storage devices, dye-sensitized solar cells, and sensors because of their high ionic conductivity and inherent thermal stability. However, measuring the conductivity of ILs by physical methods is time-consuming and expensive, whereas the use of computational screening and testing methods can be rapid and effective. In this study, we used experimentally measured and published data to construct a deep neural network capable of making rapid and accurate predictions of the conductivity of ILs. The neural network is trained on 406 unique and chemically diverse ILs. This model is one of the most chemically diverse conductivity prediction models to date and improves on previous studies that are constrained by the availability of data, the environmental conditions, or the IL base. Feature engineering techniques were employed to identify key chemo-structural characteristics that correlate positively or negatively with the ionic conductivity. These features are capable of being used as guidelines to design and synthesize new highly conductive ILs. This work shows the potential for machine-learning models to accelerate the rate of identification and testing of tailored, high-conductivity ILs.

Chemistry Dissolved in Ionic Liquids. A Theoretical Perspective

The theoretical treatment of ionic liquids must focus now on more realistic models while at the same time keeping an accurate methodology when following recent ionic liquids research trends or allowing predictability to come to the foreground. In this Perspective, we summarize in three cases of advanced ionic liquid research what methodological progress has been made and point out difficulties that need to be overcome. As particular examples to discuss we choose reactions, chirality, and radicals in ionic liquids. All these topics have in common that an explicit or accurate treatment of the electronic structure and/or intermolecular interactions is required (accurate methodology), while at the same time system size and complexity as well as simulation time (realistic model) play an important role and must be covered as well.