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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21 April 2022
Research Article|
April 18 2022
Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures
Dung Viet Duong
;
Dung Viet Duong
1
School of Engineering, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
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Hung-Vu Tran
;
Hung-Vu Tran
2
Department of Chemistry, University of Houston
, 4800 Calhoun Road, Houston, Texas 77204-5003, USA
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Sachini Kadaoluwa Pathirannahalage;
Sachini Kadaoluwa Pathirannahalage
3
School of Science, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
4
Laboratoire de Chimie, Ecole Normale Supérieure de Lyon, CNRS
, Lyon 69342, France
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Stuart J. Brown;
Stuart J. Brown
3
School of Science, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
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Michael Hassett
;
Michael Hassett
3
School of Science, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
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Dilek Yalcin;
Dilek Yalcin
5
CSIRO Manufacturing
, Clayton, VIC 3168, Australia
6
Department of Chemistry and Physics, Centre for Materials and Surface Science, School of Molecular Sciences, La Trobe University
, Melbourne, Victoria 3086, Australia
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Nastaran Meftahi
;
Nastaran Meftahi
7
ARC Centre of Excellence in Exciton Science, School of Science, RMIT University
, Melbourne, VIC 3001, Australia
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Andrew J. Christofferson
;
Andrew J. Christofferson
3
School of Science, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
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Tamar L. Greaves
;
Tamar L. Greaves
a)
3
School of Science, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
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Dung Viet Duong
1
Hung-Vu Tran
2
Sachini Kadaoluwa Pathirannahalage
3,4
Stuart J. Brown
3
Michael Hassett
3
Dilek Yalcin
5,6
Nastaran Meftahi
7
Andrew J. Christofferson
3
Tamar L. Greaves
3,a)
Tu C. Le
1,a)
1
School of Engineering, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
2
Department of Chemistry, University of Houston
, 4800 Calhoun Road, Houston, Texas 77204-5003, USA
3
School of Science, STEM College, RMIT University
, GPO Box 2476, Melbourne, VIC 3001, Australia
4
Laboratoire de Chimie, Ecole Normale Supérieure de Lyon, CNRS
, Lyon 69342, France
5
CSIRO Manufacturing
, Clayton, VIC 3168, Australia
6
Department of Chemistry and Physics, Centre for Materials and Surface Science, School of Molecular Sciences, La Trobe University
, Melbourne, Victoria 3086, Australia
7
ARC Centre of Excellence in Exciton Science, School of Science, RMIT University
, Melbourne, VIC 3001, Australia
J. Chem. Phys. 156, 154503 (2022)
Article history
Received:
January 18 2022
Accepted:
March 25 2022
Citation
Dung Viet Duong, Hung-Vu Tran, Sachini Kadaoluwa Pathirannahalage, Stuart J. Brown, Michael Hassett, Dilek Yalcin, Nastaran Meftahi, Andrew J. Christofferson, Tamar L. Greaves, Tu C. Le; Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures. J. Chem. Phys. 21 April 2022; 156 (15): 154503. https://doi.org/10.1063/5.0085592
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