Symmetry is a fundamental concept within chemistry; molecules’ idealized symmetry can shift, based on molecular environment. In the crystallographic solid state, molecules often show dissymmetric conformations, owing to intra- and intermolecular interactions. The molecular shape of these structures’ bending is often described using a combination of language, selected distances and angles. This manual approach is cumbersome when comparing more than a handful of structures. Studies we have recently published with the normal-structural decomposition (NSD) algorithm have demonstrated that a normal-coordinate-along-symmetric-modes approach is a powerful method of structure modelling for porphyrins. Our next-generation approach of Symmetric Coordinate Structural Decomposition (SCSD) is a generalization of an NSD-type algorithm to near-symmetric molecules in the point-groups C2v, C2h, D2h and D4h. Using this approach, database-wide crystal structure comparisons, normal-coordinate analysis and machine learning can be performed with ease, and new structures can be easily compared to precomputed sets. This simple parameterization of molecular structure in “irreducible” terms has application in many disparate subfields – from photomedicine to molecular electronics – in the correlation of molecular structure and measured properties. This talk will present the algorithm, test cases of database-scale analysis and the potential for molecular shape prediction with data science.

A general-form pre-treatment algorithm for comparing near-symmetric molecules

ACS Spring Meeting 2021

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