  Surfactants are of high importance in different industrial sectors such as
cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many
quantitative structure-property relationship (QSPR) models have been developed
for surfactants. Each predictive model typically focuses on one surfactant
class, mostly nonionics. Graph Neural Networks (GNNs) have exhibited a great
predictive performance for property prediction of ionic liquids, polymers and
drugs in general. Specifically for surfactants, GNNs can successfully predict
critical micelle concentration (CMC), a key surfactant property associated with
micellization. A key factor in the predictive ability of QSPR and GNN models is
the data available for training. Based on extensive literature search, we
create the largest available CMC database with 429 molecules and the first
large data collection for surface excess concentration ($\Gamma$$_{m}$),
another surfactant property associated with foaming, with 164 molecules. Then,
we develop GNN models to predict the CMC and $\Gamma$$_{m}$ and we explore
different learning approaches, i.e., single- and multi-task learning, as well
as different training strategies, namely ensemble and transfer learning. We
find that a multi-task GNN with ensemble learning trained on all $\Gamma$$_{m}$
and CMC data performs best. Finally, we test the ability of our CMC model to
generalize on industrial grade pure component surfactants. The GNN yields
highly accurate predictions for CMC, showing great potential for future
industrial applications.
