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

• Published in: The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory Vol. 122; no. 51; pp. 9851 - 9868
• Main Authors: Choi, Yeseul, Adamczyk, Andrew J
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27-12-2018
• ISSN: 1089-5639; 1520-5215
• DOI: 10.1021/acs.jpca.8b09797

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 x Ge y H z , 1 < X + Y ≤ 6) to predict the structural, thermochemical, and electronic properties. The optimized geometries of the Si x Ge y H z 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 x Ge y H z clusters. These properties will be discussed in the context of tailored nanomaterials design and generalized using a machine learning approach.