Machine learning models and predictions for a nucleophilic substitution reaction in ionic liquids

Publication title:

The
ability to tailor the constituent ions in ionic liquids (ILs) is highly
advantageous as it provides access to solvents with a range of physicochemical
properties. However, this benefit also leads to large compositional spaces that
need to be explored to optimise systems, often involving time consuming
experimental work. The use of machine learning methods is an effective way to
gain insight based on existing data, to develop structure-property relationships
and to allow the prediction of ionic liquid properties. Here we have applied
machine learning models to experimentally determined rate constants of a
representative organic process (the reaction of pyridine with benzyl bromide)
in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial
neural networks (BRANNLP) were both able to model the data well. The MLREM
model was able to identify the structural features on the cations and anions
that had the greatest effect on the rate constant. Secondly, predictive MLREM
and BRANNLP models were developed from the full initial set of rate constant
data. From these models, a large number of predictions (>9000) of rate
constant were made for mixtures of different ionic liquids, at different proportions
of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested
experimentally, including through the preparation of novel ionic liquids, with
overall good agreement between the predicted and experimental data. This study
highlights the benefits of using machine learning methods on kinetic data in
ionic liquid mixtures to enable the development of rigorous structure-property
relationships across multiple variables simultaneously, and to predict properties
of new ILs and experimental conditions.