A green solvent screening tool for emerging materials via uncertainty aware, transformer enhanced transfer learning

Accurate prediction of solubility remains a central challenge across materials science and sustainable chemistry. In particular, due to emerging technologies like organic and hybrid photovoltaics, batteries, and catalysis, solvent usage is expected to increase significantly within the coming years. Therefore, substituting solvents with greener alternatives is vital. This is where machine learning can make a substantial impact. However, the limited data on critical parameters of solubility significantly constraints machine learning efficacy. In this work, we transfer a pre-trained foundational model on QM9 targets to our application with minimal data requirements. Additionally, the pipeline integrates uncertainty quantification, allowing the user to gauge the confidence of the predictions. As baseline, we succeed in predicting the Hansen solubility parameters and Dielectric Constant for which extensive databases exist. Importantly, we achieve high model performance on additional targets, such as Gutmann Donor and Acceptor numbers, where the available data is extremely limited. Overall, we augment data on solubility descriptors by up to two orders of magnitude with high-quality predictions. For effective dissemination, we deploy an easy-to-use, easily integratable with high throughput labs, customizable tool for ranking and screening possible solvent substitutes. Finally, we both rediscovered known green solvent alternatives and proposed new candidates, proving its relevance for finding eco-friendly solvents.