Studies on deep eutectic solvents (DESs), a new class
of “green”
solvents, are attracting increasing attention from researchers, as
evidenced by the rapidly growing number of publications in the literature.
One of the main advantages of DESs is that they are tailor-made solvents,
and therefore, the number of potential DESs is extremely large. It
is essential to have computational methods capable of predicting the
physicochemical properties of DESs, which are needed in many industrial
applications and research. Surface tension is one of the most important
properties required in many applications. In this work, we report
a relatively generalized artificial neural network (ANN) for predicting
the surface tension of DESs. The database used can be considered comprehensive
because it contains 1571 data points from 133 different DES mixtures
in 520 compositions prepared from 18 ions and 63 hydrogen bond donors
in a temperature range of 277–425 K. The ANN model uses molecular
parameter inputs derived from the conductor-like screening model for
real solvents (
S
σ-profiles
). The training and testing results show that the best performing
ANN architecture consisted of two hidden layers with 15 neurons each
(9–15–15–1). The proposed ANN was excellent in
predicting the surface tension of DESs, as
R
2
values of 0.986 and 0.977 were obtained for training and
testing, respectively, with an overall average absolute relative deviation
of 2.20%. The proposed models represent an initiative to promote the
development of robust models capable of predicting the properties
of DESs based only on molecular parameters, leading to savings in
investigation time and resources.
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