A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils

The
viscosities of pure liquids are estimated at 25 °C, from
their molecular structures, using three modeling approaches: group
contributions, COSMO-RS σ-moment-based neural networks, and
graph machines. The last two are machine-learning methods, whereby
models are designed and trained from a database of viscosities of
300 molecules at 25 °C. Group contributions and graph machines
make use of the 2D-structures only (the SMILES codes of the molecules),
while neural networks estimations are based on a set of five descriptors:
COSMO-RS σ-moments. For the first time, leave-one-out is used
for graph machine selection, and it is shown that it can be replaced
with the much faster virtual leave-one-out algorithm. The database
covers a wide diversity of chemical structures, namely, alkanes, ethers,
esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes,
as well as different chemical backbone, i.e., straight, branched,
or cyclic chains. A comparison of the viscosities of liquids of an
independent set of 22 cosmetic oils shows that the graph machine approach
provides the most accurate results given the available data. The results
obtained by the neural network based on sigma-moments and by the graph
machines can be duplicated easily by using a demonstration tool based
on the Docker technology, available for download as explained in the Supporting
Information. This demonstration also allows the reader
to predict, at 25 °C, the viscosity of any liquid of moderate
molecular size (M < 600 Da) that contains C, H,
O, or Si atoms, starting either from its SMILES code or from its σ-moments
computed with the COSMOtherm software.