| Deeper understanding of complex chemical systems can be aided by detailed kinetic modeling, in which processes are broken down into their individual elementary reactions. An important industrial goal is to move from postdictive to predictive modeling, where new chemical vapor deposition (CVD) precursors, for example, can be tested for efficiency without performing tedious and expensive experiments. Some of these microkinetic models may contain hundreds of reacting chemical species, and thousands of reactions; thus, it is desirable to build the models automatically with a computer to speed up model generation and reduce errors. Automatic mechanism generation is now commonly used for applications such as combustion, but extension to other systems presents challenges. This dissertation describes the extension of the Reaction Mechanism Generator (RMG) software to two less-studied chemical systems: the oxidation of liquid fuels and the gas-phase decomposition of silicon hydrides.;To model liquid fuel oxidation, the software's existing gas-phase thermodynamics and kinetics databases needed to be supplemented, or corrected to account for solvated reactions. Existing correlations and data for solvation thermodynamics and diffusion were improved and added to RMG. Solvation kinetics data were obtained by developing a machine learning algorithm to systematically predict the change in barrier height when going from gas-phase to various solvents. The algorithm was trained with quantum chemistry calculations on a simple set of hydrogen abstraction and intra-hydrogen migration reactions. The method was used to change the rates in a model for the oxidation of dodecane/methyl oleate blends, showing a marked change in the model's prediction for the fuel's induction period.;The second part of this dissertation involves gas-phase silicon hydride decomposition, for the application to CVD. Thermodynamic and kinetic data were added from literature to RMG's database. Specifically focusing on radical reaction types, additional data were calculated via quantum chemistry for hydrogen bond increment (HBI) values of silicon hydride species, as well as hydrogen abstraction reaction rates. A SiH4 decomposition model was built with the updated RMG and compared to experiment, with good agreement.;This work provides new insight on both of these chemical systems and contributes new calculated thermodynamics and kinetics parameters. Importantly, it also guides future developers in adding capabilities for new phases or elements to mechanism generation software. |
