Predicting the Temperature Dependence of Surfactant CMCs Using Graph
  Neural Networks
Christoforos Brozos Jan G. Rittig Sandip Bhattacharya Elie Akanny Christina Kohlmann Alexander Mitsos
The critical micelle concentration (CMC) of surfactant molecules is an
essential property for surfactant applications in industry. Recently, classical
QSPR and Graph Neural Networks (GNNs), a deep learning technique, have been
successfully applied to predict the CMC of surfactants at room temperature.
However, these models have not yet considered the temperature dependency of the
CMC, which is highly relevant for practical applications. We herein develop a
GNN model for temperature-dependent CMC prediction of surfactants. We collect
about 1400 data points from public sources for all surfactant classes, i.e.,
ionic, nonionic, and zwitterionic, at multiple temperatures. We test the
predictive quality of the model for following scenarios: i) when CMC data for
surfactants are present in the training of the model in at least one different
temperature, and ii) CMC data for surfactants are not present in the training,
i.e., generalizing to unseen surfactants. In both test scenarios, our model
exhibits a high predictive performance of R$^2 \geq $ 0.94 on test data. We
also find that the model performance varies by surfactant class. Finally, we
evaluate the model for sugar-based surfactants with complex molecular
structures, as these represent a more sustainable alternative to synthetic
surfactants and are therefore of great interest for future applications in the
personal and home care industries.