Efficiency of an unsupervised machine learning approach for superatomic cluster assessment

Abstract

Superatoms are of broad interest in materials science due to their high tunability of electronic properties upon structural modification. The quantum-mechanical (QM) descriptors estimated in our previous work (Sikorska C, Puzyn T, Nanotechnology (2015), 26: 455702) have been used to explore structure-property relationships in superatom-like fullerenes. The structural similarity among fullerene derivatives has been investigated using principal component analysis (PCA) and two-way Hierarchical Cluster Analysis (t-HCA) based on these QM descriptors, demonstrating how electronic structure parameters and geometrical features influence fullerene cluster properties. This unsupervised machine learning approach highlights that descriptors derived from quantum-mechanical calculations enable distinguishing groups of structurally similar compounds for which we can assume similar values ​​of selected physicochemical properties. In addition, the use of computational methods not only reduces the time and costs of research but also the amount of waste generated during experimental analyses. Hence, the research described has significant social and economic significance. At the same time, our results provide a framework for understanding structure-property relationships in nanomaterials that can be used in the future to define new Quantitative Structure-Property Relationship (QSPR) models for predicting physicochemical properties of fullerene-based materials directly from the fullerene structure.