| Abstract
| - Automated mechanism generation is an attractive way to understand the fundamental kinetics of complexreaction systems such as silicon hydride clustering chemistry. It relies on being able to tell molecules apartas they are generated. The graph theoretic foundation allows molecules to be identified using unique notationscreated from their connectivity. To apply this technique to silicon hydride clustering chemistry, a moleculecanonicalization and encoding algorithm was developed to handle complex polycyclic, nonplanar species.The algorithm combines the concepts of extended connectivity and the idea of breaking ties to encodehighly symmetric molecules. The connected components in the molecules are encoded separately andreassembled using a depth-first search method to obtain the correct string codes. A revised cycle-findingalgorithm was also developed to properly select the cycles used for ring corrections when thermodynamicproperties were calculated using group additivity. In this algorithm, the molecules are expressed explicitlyas trees, and all linearly independent cycles of every size in the molecule are found. The cycles are thensorted according to their size and functionality, and the cycles with higher priorities will be used to includering corrections. Applying this algorithm, more appropriate cycle selection and more accurate estimation ofthermochemical properties of the molecules can be obtained.
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