American Chemical Society
jp2c08232_si_002.pdf (2.88 MB)

Can Machine Learning Predict the Phase Behavior of Surfactants?

Download (2.88 MB)
journal contribution
posted on 2023-04-12, 16:11 authored by Joseph C. R. Thacker, David J. Bray, Patrick B. Warren, Richard L. Anderson
We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three nonionic surfactants. 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 for entire de novo phase diagram prediction. Although some machine learning 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 an 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.