An accessible property classification framework to predict the solubility of functionalised naphthalenes and rylenes in organic solvents.

The development of functional materials such as multilayer films from supramolecular self-assembling materials is hindered by the current lack of design principles and predictability. Quantitative structure-property relationships have emerged as a useful technique to tie properties of interest to the molecular structure across various applications but have not been widely explored in supramolecular material problems. Herein we describe and share an accessible, visual programming, classification framework which we have applied to the prediction of solubility of amino acid-functionalised napthalenes and rylenes. We show through a proof-of-concept dataset for amino acid-functionalised naphthalenes and rylenes that solubility can be effectively classified with sufficient high-quality training data, and highlight the need for standardised characterisation procedures in the field of supramolecular chemistry if more complex and informative quantitative structure-property relationships are to be developed. Such models could ultimately afford the tailored design of functional materials.