Estimation of viscosities of pure ionic liquids using an artificial neural network based on only structural characteristics

In this work, a three layer feed-forward artificial neural network, with 24 neurons, was constructed to estimate the viscosities of a wide range of ionic liquid families, including those based on the imidazolium, ammonium, pyridinium, pyrrolidinium, phosphonium, and isoquinolinium cations, together with various anions, as well as varying lengths of alkyl side-chain lengths. The model is a function of the molecular weight and structure of the ionic liquid, and the system conditions of temperature and pressure. It covers a temperature range of (273.15 to 393.15) K and a pressure range of (0.1 to 150) MPa. Results indicated the estimated values of viscosities of pure ionic liquids to be in good agreement with the experimental data. The training (correlating) and validation coefficients (R) were 1.00000 and 0.99955, respectively, while the training and validation performances (MSE) on the training and validation datasets were 4.36 × 10− 8, and 1.63 × 10− 6, respectively. The average absolute error value on the test dataset was 1.310%.