Predicting the Mixing Behavior of Aqueous Solutions Using a Machine Learning Framework

The
most direct approach to determining if two aqueous solutions
will phase-separate upon mixing is to exhaustively screen them in
a pair-wise fashion. This is a time-consuming process that involves
preparation of numerous stock solutions, precise transfer of highly
concentrated and often viscous solutions, exhaustive agitation to
ensure thorough mixing, and time-sensitive monitoring to observe the
presence of emulsion characteristics indicative of phase separation.
Here, we examined the pair-wise mixing behavior of 68 water-soluble
compounds by observing the formation of microscopic phase boundaries
and droplets of 2278 unique 2-component solutions. A series of machine
learning classifiers (artificial neural network, random forest, k-nearest
neighbors, and support vector classifier) were then trained on physicochemical
property data associated with the 68 compounds and used to predict
their miscibility upon mixing. Miscibility predictions were then compared
to the experimental observations. The random forest classifier was
the most successful classifier of those tested, displaying an average
receiver operator characteristic area under the curve of 0.74. The
random forest classifier was validated by removing either one or two
compounds from the input data, training the classifier on the remaining
data and then predicting the miscibility of solutions involving the
removed compound(s) using the classifier. The accuracy, specificity,
and sensitivity of the random forest classifier were 0.74, 0.80, and
0.51, respectively, when one of the two compounds to be examined was
not represented in the training data. When asked to predict the miscibility
of two compounds, neither of which were represented in the training
data, the accuracy, specificity, and sensitivity values for the random
forest classifier were 0.70, 0.82 and 0.29, respectively. Thus, there
is potential for this machine learning approach to improve the design
of screening experiments to accelerate the discovery of aqueous two-phase
systems for numerous scientific and industrial applications.