Issue 1, 2018

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

Abstract

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%.

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

Supplementary files

Article information

Article type
Paper
Submitted
06 sep. 2017
Accepted
12 ene. 2018
First published
12 ene. 2018

Mol. Syst. Des. Eng., 2018,3, 253-263

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

W. Beckner, C. M. Mao and J. Pfaendtner, Mol. Syst. Des. Eng., 2018, 3, 253 DOI: 10.1039/C7ME00094D

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