A dataset for aqueous surfactant phase behavior as a function of temperature and composition

Presented here is a dataset (PhDat) discretizing the aqueous phase behavior of 143 surfactants as a function of temperature and composition. Across the complete dataset, we classify the discretized state points into 118 distinct possible phase states, comprising both single- and two-phase regions, taking a probabilistic approach to describe phase transitions and narrow biphasic gaps. We also outline the workflow adopted to obtain the digitized phase diagrams. We anticipate this dataset will be useful for machine learning or similar applications, in the practically important field of surfactant formulation. The dataset has been designed to be extensible such that it can accommodate a wider variety of surfactant mixtures or non-surfactant molecule phase diagrams.