Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures.
J Chem Phys
; 156(15): 154503, 2022 Apr 21.
Article
em En
| MEDLINE
| ID: mdl-35459305
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Líquidos Iônicos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Chem Phys
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Austrália