Planting the Seeds of a Decision Tree for Ionic Liquids: Steric and Electronic Impacts on Melting Points of Triarylphosponium Ionic Liquids.

While machine learning and artificial intelligence offer promising avenues in the computer-aided design of materials, the complexity of these computational techniques remains a barrier for scientists outside of the specific fields of study. Leveraging decision tree models, inspired by empirical methodologies, offers a pragmatic solution to the knowledge barrier presented by artificial intelligence (AI). Herein, we present a model allowing for the qualitative prediction of melting points of ionic liquids derived from the crystallographic analysis of a series of phosphonium-based ionic liquids. By carefully tailoring the steric and electronic properties of the cations within these salts, trends in the melting points are observed, pointing toward the critical importance of π interactions to forming the solid state. Quantification of the percentage of these π interactions using modern quantum crystallographic approaches reveals a linear trend in the relationship of C-Hπ and π-π stacking interactions with melting points. These structure-property relationships are further examined by using computational studies, helping to demonstrate the inverse relationship of dipole moments and melting points for ionic liquids. The results provide valuable insights into the features and relationships that are consistent with achieving low