Statistical models are able to predict ionic liquid viscosity across a wide range of chemical functionalities and experimental conditions

Herein we present a method of developing predictive models of viscosity for ionic liquids (ILs) using publicly available data in the ILThermo database and the open-source software toolkits PyChem, RDKit, and SciKit-Learn. The process consists of downloading ∼700 datapoints from ILThermo, generating ∼1200 physiochemical features with PyChem and RDKit, selecting 11 features with the least absolute shrinkage selection operator (LASSO) method, and using the selected features to train a multi-layer perceptron regressor-a class of feedforward artificial neural network (ANN). The interpretability of the LASSO model allows a physical interpretation of the model development framework while the flexibility and non-linearity of the hidden layer of the ANN optimizes performance. The method is tested on a range of temperatures, pressures, and viscosities to evaluate its efficacy in a general-purpose setting. The model was trained on 578 datapoints including a temperature range of 273.15-373.15 K, pressure range of 60-160 kPa, viscosity range of 0.0035-0.993 Pa s, and ILs of imidazolium, phosphonium, pyridinium, and pyrrolidinium classes to give 33 different salts altogether. The model had a validation set mean squared error of 4.7 × 10 −4 ± 2.4 × 10 −5 Pa s or relative absolute average deviation of 7.1 ± 1.3%. Herein we present a method of developing predictive models of viscosity for ionic liquids (ILs) using publicly available data in the ILThermo database and the open-source software toolkits PyChem, RDKit, and SciKit-Learn.