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.

翻译：表面活性剂在化妆品、洗涤剂、石油开采及药物递送等不同工业领域中具有高度重要性。因此，研究者已开发出大量针对表面活性剂的定量构效关系（QSPR）模型，但每个预测模型通常仅关注单一表面活性剂类别，其中非离子型占绝大多数。图神经网络（GNNs）在离子液体、聚合物及药物性质预测中展现出卓越性能。针对表面活性剂，GNNs可成功预测与胶束化相关的关键性质——临界胶束浓度（CMC）。QSPR与GNN模型预测能力的关键因素在于可用于训练的数据规模。通过广泛的文献检索，我们构建了包含429个分子的最大CMC数据库，以及首个包含164个分子的表面过剩浓度（$\Gamma$$_{m}$）大型数据集——该参数是与发泡性能相关的另一表面活性剂性质。在此基础上，我们开发了用于预测CMC与$\Gamma$$_{m}$的GNN模型，探索了单任务与多任务学习等不同学习方法，以及集成学习和迁移学习等不同训练策略。研究表明，采用集成学习且基于全部$\Gamma$$_{m}$和CMC数据训练的多任务GNN模型表现最优。最后，我们测试了CMC模型对工业级纯组分表面活性剂的泛化能力。该GNN模型可对CMC实现高度精确预测，展现出未来工业应用的巨大潜力。