One of the limitations of our previous work, where we have featurised formulations without any structural information, is that the trained models cannot be generalised to any new ingredients without effectively re-starting the experimental campaign . Hence, we would like to find a more generalisable featurisation for the surfactants, so that we can make in-silico predictions for a new drop-in replacement. As small organic molecules, there are many methods for featurising this type of ingredient with i) string-based representations, e.g., SMILES ; ii) molecular graphs ; and iii) molecular features (from 0D to 3D descriptors) . This list is not exhaustive. There are in particular many different cheminformatics packages that can enumerate large numbers of descriptors, which should be feature engineered down to a more sensible subset relative to the dataset size available for training. However, many of these featurisations require large training datasets or are not directly interpretable.