This project develops Graph Machine Learning models to predict Kamlet–Taft solvation parameters of ionic liquids, which describe key solvent properties such as polarity and hydrogen bonding ability. Ionic liquids are represented as molecular graphs, and Graph Neural Networks are used to learn structural embeddings, combined with quantum chemical descriptors, to build predictive and interpretable models. The goal is to link molecular structure to solvation behavior and enable prediction for new, previously uncharacterized ionic liquids.

Requirements: Strong interest in graph machine learning with application to the chemical domain

Contact: Nils Kriege