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Duong DV, Tran HV, Pathirannahalage SK, Brown SJ, Hassett M, Yalcin D, Meftahi N, Christofferson AJ, Greaves TL, Le TC. Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures. J Chem Phys 2022; 156:154503. [PMID: 35459305 DOI: 10.1063/5.0085592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.
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Affiliation(s)
- Dung Viet Duong
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Hung-Vu Tran
- Department of Chemistry, University of Houston, 4800 Calhoun Road, Houston, Texas 77204-5003, USA
| | | | - Stuart J Brown
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Michael Hassett
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Dilek Yalcin
- CSIRO Manufacturing, Clayton, VIC 3168, Australia
| | - Nastaran Meftahi
- ARC Centre of Excellence in Exciton Science, School of Science, RMIT University, Melbourne, VIC 3001, Australia
| | - Andrew J Christofferson
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Tamar L Greaves
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
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Greaves TL, Schaffarczyk McHale KS, Burkart-Radke RF, Harper JB, Le TC. Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids. Phys Chem Chem Phys 2021; 23:2742-2752. [PMID: 33496292 DOI: 10.1039/d0cp04227g] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.
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Affiliation(s)
- Tamar L Greaves
- College of Science Engineering and Health, RMIT University, Melbourne, VIC 3001, Australia.
| | | | | | - Jason B Harper
- School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia.
| | - Tu C Le
- College of Science Engineering and Health, RMIT University, Melbourne, VIC 3001, Australia.
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Beckner W, Ashraf C, Lee J, Beck DAC, Pfaendtner J. Continuous Molecular Representations of Ionic Liquids. J Phys Chem B 2020; 124:8347-8357. [DOI: 10.1021/acs.jpcb.0c05938] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Wesley Beckner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98105, United States
| | - Chowdhury Ashraf
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98105, United States
| | - James Lee
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98105, United States
| | - David A. C. Beck
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98105, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98105, United States
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