Predicting thermophysical properties of dialkylimidazolium ionic liquids from sigma profiles
| Autor: | Mark A. Stadtherr, Joan F. Brennecke, Pratik Kelkar, Yuanyuan Lyu, Michael Baldea, Oscar Nordness |
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| Rok vydání: | 2021 |
| Předmět: |
Materials science
Atmospheric pressure Sigma 02 engineering and technology Conductivity 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Atomic and Molecular Physics and Optics 0104 chemical sciences Electronic Optical and Magnetic Materials Support vector machine chemistry.chemical_compound Viscosity chemistry Ionic liquid Materials Chemistry Range (statistics) Radial basis function Physical and Theoretical Chemistry 0210 nano-technology Biological system Spectroscopy |
| Zdroj: | Journal of Molecular Liquids. 334:116019 |
| ISSN: | 0167-7322 |
| DOI: | 10.1016/j.molliq.2021.116019 |
| Popis: | We present a Support Vector Regression (SVR) machine learning framework for predicting the viscosity, ionic conductivity, and density of imidazolium ionic liquids (ILs) using a universal set of features extracted from COSMO-RS sigma profiles. To train and test the SVR model, we assembled three property datasets with approximately 40 different ILs, each consisting of over 1000 experimental datapoints measured across a wide range of temperatures and pressures. From calculated sigma profiles we extract IL descriptors or “features” that are readily fit by using the SVR model. After cleaning of the measurement datasets and selecting these IL features, we compare the performance of the radial basis function (RBF) and linear kernels using a standard k-fold cross-validation to separate the respective datasets into training and testing datasets without bias. Using these results, we demonstrate the ability of the RBF-SVR model to predict the viscosity, conductivity, and density of unobserved ILs at atmospheric pressure. |
| Databáze: | OpenAIRE |
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