GFNn-xTBT is a semiempirical quantum chemical method, which incorporates multipole electrostatics and density-dependent dispersion contributions. Here, the prediction capabilities of this new semiempirical approach are evaluated by computing the transition frequencies of vibrational modes in different vibrational probes. To this end, this study focuses on the application of GFNn-xTB to molecular system embedded in both explicit and implicit solvents. In particular, the benchmarking of GFNn-XTB is performed using the transition frequencies of highly localized vibrational modes (i.e., amide I, and nitrile and hydroxyl stretches) of well-known vibrational probes. Our results show that the GFNn-xTB methods provide a robust, efficient, and generally applicable approach for calculating vibrational frequencies. In particular, the GFNn-xTB methods appears to quantitatively reproduce the mean frequencies transition frequencies when implicit solvent effects are included in the computations. More importantly, the use of vibrational modes as benchmarks shows the possibility of using vibrational spectroscopy as experimental input for deriving implicit solvent models with more accurate performance at the molecular level. Overall, GFNn-xTB is observed to a be robust methodology for performing large-scale quantum chemical computations of the vibrational frequencies of solvated organic molecules solvated. In addition, the high accuracies and low computational time cost of GFNn-xTB enables the fast production large vibrational libraries.

Benchmarking semiempirical quantum mechanical GFNn-xTB using vibrational spectroscopy

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