Analyzing Spectral Similarities for Structural Identification Using a New Benchmark Database.

Vibrational spectra, characterized by structurally sensitive features, offer critical insights into molecular structures, bonding, and dynamics. Yet, interpreting measured spectra and identifying corresponding structures require theoretical equivalents and quantitative analysis. Here, we introduce a new experimental database that includes broad-range ionization-detected stimulated Raman scattering signatures besides harmonic Raman frequencies calculated with widely used density functional methods/basis sets. By comparing experimental fundamental bands and computed data, we derive single global and multiple range- and mode-dependent scaling factors and analyze the resulting error distributions, showing that mode-dependent scaling provides the greatest accuracy. Additionally, we explore various methods for evaluating similarities between measured fundamental spectra of different compounds and calculated data sets of conformers. Our findings indicate that Euclidean and Manhattan distance metrics for frequencies and intensities uncover subtle structural variations, yielding spectral similarity rankings that facilitate structural identifications. This new database and methodology address key challenges in spectral assignment, and we anticipate that they will serve as benchmarks for future predictive models and foster the development of advanced strategies.