The effect of descriptor choice in machine learning models for ionic liquid melting point prediction.

Abstract

The characterization of an ionic liquid's properties based on structural information is a longstanding goal of computational chemistry, which has received much focus from ab initio and molecular dynamics calculations. This work examines kernel ridge regression models built from an experimental dataset of 2212 ionic liquid melting points consisting of diverse ion types. Structural descriptors, which have been shown to predict quantum mechanical properties of small neutral molecules within chemical accuracy, benefit from the addition of first-principles data related to the target property (molecular orbital energy, charge density profile, and interaction energy based on the geometry of a single ion pair) when predicting the melting point of ionic liquids. Out of the two chosen structural descriptors, ECFP4 circular fingerprints and the Coulomb matrix, the addition of molecular orbital energies and all quantum mechanical data to each descriptor, respectively, increases the accuracy of surrogate models for melting point prediction compared to using the structural descriptors alone. The best model, based on ECFP4 and molecular orbital energies, predicts ionic liquid melting points with an average mean absolute error of 29 K and, unlike group contribution methods, which have achieved similar results, is applicable to any type of ionic liquid.