Abstract
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.
Published Version
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