Tuning Hydrogenated Silicon, Germanium, and SiGe Nanocluster Properties Using Theoretical Calculations and a Machine Learning Approach.

There are limited studies available that predict the properties of hydrogenated silicon-germanium (SiGe) clusters. For this purpose, we conducted a computational study of 46 hydrogenated SiGe clusters (Si Ge H , 1 < X + Y ≤ 6) to predict the structural, thermochemical, and electronic properties. The optimized geometries of the Si Ge H clusters were investigated using quantum chemical calculations and statistical thermodynamics. The clusters contained 6 to 9 fused Si-Si, Ge-Ge, or Si-Ge bonds, i.e., bonds participating in more than one 3- to 4-membered rings, and different degrees of hydrogenation, i.e., the ratio of hydrogen to Si/Ge atoms varied depending on cluster size and degree of multifunctionality. Our studies have established trends in standard enthalpy of formation, standard entropy, and constant pressure heat capacity as a function of cluster composition and structure. A novel bond additivity correction model for SiGe chemistry was regressed from experimental data on seven acyclic Si/Ge/SiGe species to improve the accuracy of the standard enthalpy of formation predictions. Electronic properties were investigated by analysis of the HOMO-LUMO energy gap to study the effect of elemental composition on the electronic stability of Si Ge H clusters. These properties will be discussed in the context of tailored nanomaterials design and generalized using a machine learning approach.