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 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.