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A knowledge of various thermophysical (in particular transport) properties of ionic liquids (ILs) is crucial from the point of view of potential applications of these fluids in chemical and related industries. In this work, over 13 000 data points of temperature- and pressure-dependent viscosity of 1484 ILs were retrieved from more than 450 research papers published in the open literature in the last three decades. The data were critically revised and then used to develop and test a new model allowing in silico predictions of the viscosities of ILs on the basis of the chemical structures of their cations and anions. The model employs a two-layer feed-forward artificial neural network (FFANN) strategy to represent the relationship between the viscosity and the input variables: temperature, pressure, and group contributions (GCs). In total, the resulting GC-FFANN model employs 242 GC-type molecular descriptors that are capable of accurately representing the viscosity behavior of ILs composed of 901 distinct ions. The neural network training, validation, and testing processes, involving 90, 5, and 5% of the whole data pool, respectively, gave mean square errors of 0.0334, 0.0595, and 0.0603 log units, corresponding to squared correlation coefficients of 0.986, 0.973, and 0.972 and overall relative deviations at the level of 11.1, 13.8, and 14.7%, respectively. The results calculated in this work were shown be more accurate than those obtained with the best current GC model for viscosity of ILs described in the literature.

A detailed knowledge of reliable data on physical properties of ionic liquids (ILs) is of great importance, because ILs are still considered as potential replacements for volatile organic solvents in modern and sustainable (“greener”) processes of chemical industry. In particular, liquid density is a very important property that is required in many design problems of chemical engineering and material science. Therefore, development of new methods for estimation of density of ILs is essential. In this work we propose a new method based on generalized empirical correlation and group contributions. It was developed based on a comprehensive database of experimental data containing over 18500 data points for a great variety of 1028 ILs. The collected data covers temperature and pressure ranges of 253–473 K and 0.1–300 MPa, respectively. Molar volume at reference temperature (298.15 K) and pressure (0.1 MPa) was assumed to be additive with respect to defined set of both cationic and anionic functional groups, whereas a Tait-type equation with four adjustable parameters was adopted to describe temperature–pressure dependence of density (P–ρ–T). The model parameters, including contributions to molar volume for 177 functional groups, as well as universal coefficients describing the P–ρ–T surface, were fitted to experimental data for 828 ILs with an average absolute relative deviation (%AARD) of 0.53%. Then, the model was evaluated by a calculation of density for 200 ILs not included in the correlation set. We showed that the proposed GCM allows the accurate prediction of high pressure densities for a variety of ILs. The resulting %AARD of prediction was 0.45% which is the one of the lowest values compared with similar correlations reported in literature. Moreover, we showed that the presented method is able to accurately capture other volumetric properties of pure ILs such as molar volume and derivative properties (thermal expansion coefficient and isothermal compressibility) as well as their temperature and pressure dependencies.

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