Can machine learning predict the phase behavior of surfactants?

We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three non-ionic surfactants in line with Bell, Phil. Trans. R. Soc. A 2016, 374, 20150137. Most machine learning classifiers we tested are capable of filling in missing data in a partially complete data set. However, strong data bias and a lack of chemical space information generally lead to poorer results entire de novo phase diagram prediction. Although some machine learn- ing classifiers perform better than others, these observations are largely robust to the particular choice of algorithm. Finally we explore how de novo phase diagram prediction can be improved by the inclusion of observations from state points sampled by analogy to commonly used experimental protocols. Our results indicate what factors should be considered when preparing for machine learning prediction of surfactant phase behavior in future studies.