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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 4.7e-4 ± 2.4e-5 Pa•s or relative absolute average deviation of 7.1 ± 1.3%.

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Supplementary files

Publication details

The article was received on 06 Sep 2017, accepted on 12 Jan 2018 and first published on 12 Jan 2018


Article type: Paper
DOI: 10.1039/C7ME00094D
Citation: Mol. Syst. Des. Eng., 2018, Accepted Manuscript
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    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, Accepted Manuscript , DOI: 10.1039/C7ME00094D

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