Systematic Method for the Exploration, Representation, and Classification of the Diphenylalanine Solvatomorphic Space

An understanding of the conditions that govern the self-assembly process of peptides is a fundamental step toward the design of new nanostructures that possess interesting properties. In this work, we first synthesize and explore extensively diphenylalanine (FF) self-assembling crystals formed in different solvents (i.e., solvatomorphs) using polarized optical microscopy and transmission electron microscopy. Then, we develop a numerical method that allows an unambiguous classification of the solvatomorphs through a K-means automatic clustering method. In addition, we generate a two-dimensional (2D) representation of the solvatomorphic space together with the clustering results via a principal component analysis (PCA). The classification is based on structural similarities of solvatomorphs as revealed by the analysis of their respective infrared spectra. Among the 20 samples considered, 4 clear clusters are extracted within which the compounds show very similar crystalline structures. The information extracted allows us to assign many of the peaks that appear in the complex IR spectra of the samples considered. The implementation of the overall procedure we propose, i.e., “GAULOIS” and “REFRACT-R”, is transferable to other types of spectra and paves the way for a systematic, fast, and accurate classification method applicable to various types of experimental spectroscopic data.