SymmSpec: An Interactive Python Tool for Predicting IR and Raman Activity for Undergraduate Inorganic Chemistry

Abstract

In undergraduate inorganic chemistry, group theory and symmetry principles are used to understand vibrational modes, predict infrared (IR) and Raman bands, and accurately assign and interpret observed spectra. As a fundamental component of undergraduate inorganic chemistry education, group theory provides a foundation for understanding the bonding and spectroscopic features of molecules via symmetry operations. Students are traditionally instructed to visualize symmetry operations using three-dimensional inorganic model kits or as mental visualizations. This often leads to challenges with understanding connections between their visualizations and the associated abstract mathematics contributing to these operations to deduce the vibrational modes and their IR and Raman activity en route to the correct interpretation of the vibrational spectra. With the rising demand to integrate computational coding into undergraduate chemistry curricula to help prepare chemists for the workforce, an interactive notebook, hosted on Google Colaboratory and employing Python programming language, was developed for undergraduate students enrolled in Advanced Inorganic Chemistry. The developed notebook, SymmSpec, uses group theory to predict the IR and Raman activity of VSEPR molecules. With an aim to enhance student understanding of the complex underlying concepts of group theory while also adding introductory elements of computational coding to the curriculum, SymmSpec has been developed to aid undergraduate inorganic students in the visualization of abstract, mathematically based concepts relevant to molecular symmetry. Students enrolled in an Advanced Inorganic Chemistry course at a large research institution were recruited to participate, use the tool, and respond to a survey of their experience.