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.

通过创建含有 429 种分子的最大的 CMC 数据库和含有 164 种分子的第一个大型表面过剩浓度 ($\Gamma$$_{m}$) 数据集，我们发展了 GNN 模型来预测 CMC 和 $\Gamma$$_{m}$，通过尝试单任务和多任务学习，并采用集成学习和迁移学习等不同的训练策略，发现多任务 GNN 与集成学习在所有 $\Gamma$$_{m}$ 和 CMC 数据上训练得到的模型效果最好，最后，我们测试了 CMC 模型在工业级纯组分表面活性剂上的泛化能力，发现 GNN 对 CMC 有高度准确的预测，具有未来工业应用的巨大潜力。