Cloud Point Temperature of Thermoresponsive Systems: A Predictive Approach in Data Scarcity Conditions

In this study, we employ machine learning techniques to improve materials in data scarcity conditions. In particular, we focus on the prediction of the cloud point temperatures of polymer–water systems with thermoresponsive behavior. We compare a model trained directly on the available data with a model based on representations learned through an encoder–decoder model, in turn pre-trained on a larger dataset to generate molecular fingerprints. Our results demonstrate that the embedding-based model significantly outperforms the direct model in predicting the cloud point temperature under the data limitations imposed by rigorous curation. This approach highlights the potential of domain-informed representation learning to tackle complex materials science problems with limited data.