Nowadays, density functional theory (DFT)-based high-throughput computational approach is becoming more efficient and, thus, attractive for finding advanced materials for electrochemical applications. In this work, we illustrate how theoretical models, computational methods, and informatics techniques can be put together to form a simple DFT-based throughput computational workflow for predicting physicochemical properties of room-temperature ionic liquids. The developed workflow has been used for screening a set of 48 ionic pairs and for analyzing the gathered data. The predicted relative electrochemical stabilities, ionic charges and dynamic properties of the investigated ionic liquids are discussed in the light of their potential practical applications.

The modern computer simulations of potential green solvents of the future, involving the room temperature ionic liquids, heavily rely on density functional theory (DFT). In order to verify the appropriateness of the common DFT methods, we have investigated the effect of the self-interaction error (SIE) on the results of DFT calculations for 24 ionic pairs and 48 ionic associates. The magnitude of the SIE is up to 40 kJ mol(-1) depending on the anion choice. Most strongly the SIE influences the calculation results of ionic associates that contain halide anions. For these associates, the range-separated density functionals suppress the SIE; for other cases, the revPBE density functional with dispersion correction and triple-ζ Slater-type basis is suitable for computationally inexpensive and reasonably accurate DFT calculations.

Carbon materials have a range of properties such as high electrical conductivity, high specific surface area, and mechanical flexibility are relevant for electrochemical applications. Carbon materials are utilised in energy conversion-and-storage devices along with electrolytes of complementary properties. In this work, we study the interaction of highly concentrated electrolytes (ionic liquids) at a model carbon surface (circumcoronene) using density functional theory methods. Our results indicate the decisive role of the dispersion interactions that noticeably strengthen the circumcoronene-ion interaction. Also, we focus on the adsorption of halide anions as the electrolytes containing these ions are promising for practical use in supercapacitors and solar cells.

Ionic liquids (ILs) play a key role in many chemical applications. As regards the theoretical approach, ILs show added difficulties in calculations due to the composition of the ion pair and to the fact that they are liquids. Although density functional theory (DFT) can treat this kind of systems to predict physico-chemical properties, common versions of these methods fail to perform accurate predictions of geometries, interaction energies, dipole moments, and other properties related to the molecular structure. In these cases, dispersion and self-interaction error (SIE) corrections need to be introduced to improve DFT calculations involving ILs. We show that the inclusion of dispersion is needed to obtain good geometries and accurate interaction energies. SIE needs to be corrected to describe the charges and dipoles in the ion pair correctly. The use of range-separated functionals allows us to obtain interaction energies close to the CCSD(T) level. © 2017 Wiley Periodicals, Inc.

The molecular structure of novel ionic liquids based on cholinium amino acids (ChAA-ILs) has been analyzed. The polarization charge density for all ion pairs has been examined as a function of the hydrophobicity of the anion. The COnductor-like Screening MOdel σ-profiles and σ-potentials have been obtained and used to interpret the chemical behavior of ChAA-ILs. Some physicochemical properties such as density and viscosity have been estimated using the COnductor-like Screening MOdel for Realistic Solvation method. Furthermore, the effects of polarization on the molecular structure, physicochemical properties, and hydrophobicities have been evaluated. Finally, the results obtained have been compared with experimental data.