Encoding of polycyclic Si-containing molecules for determining species uniqueness in automated mechanism generation

Automated mechanism generation is an attractive way to understand the fundamental kinetics of complex reaction systems such as silicon hydride clustering chemistry. It relies on being able to tell molecules apart as they are generated. The graph theoretic foundation allows molecules to be identified using unique notations created from their connectivity. To apply this technique to silicon hydride clustering chemistry, a molecule canonicalization 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 encode highly symmetric molecules. The connected components in the molecules are encoded separately and reassembled using a depth-first search method to obtain the correct string codes. A revised cycle-finding algorithm was also developed to properly select the cycles used for ring corrections when thermodynamic properties were calculated using group additivity. In this algorithm, the molecules are expressed explicitly as trees, and all linearly independent cycles of every size in the molecule are found. The cycles are then sorted according to their size and functionality, and the cycles with higher priorities will be used to include ring corrections. Applying this algorithm, more appropriate cycle selection and more accurate estimation of thermochemical properties of the molecules can be obtained.