Anharmonic quantum nuclear densities from full dimensional vibrational eigenfunctions with application to protonated glycine

Abstract

The interpretation of molecular vibrational spectroscopic signals in terms of atomic motion is essential to understand molecular mechanisms and for chemical characterization. The signals are usually assigned after harmonic normal mode analysis, even if molecular vibrations are known to be anharmonic. Here we obtain the quantum anharmonic vibrational eigenfunctions of the 11-atom protonated glycine molecule and we calculate the density distribution of its nuclei and its geometry parameters, for both the ground and the O-H stretch excited states, using our semiclassical method based on ab initio molecular dynamics trajectories. Our quantum mechanical results describe a molecule elongated and more flexible with respect to what previously thought. More importantly, our method is able to assign each spectral peak in vibrational spectroscopy by showing quantitatively how normal modes involving different functional groups cooperate to originate that spectroscopic signal. The method will possibly allow for a better rationalization of experimental spectroscopy.

How many water molecules are needed to solvate one?

“…Vibrational spectroscopy provides a powerful experimental and theoretical tool to get insights into the molecular structure. 7 In liquid water, the infrared (IR) spectrum is characterized by a broad band for the stretches, located between 3200 and 3550 cm −1 , which is strongly red shifted with respect to the corresponding lines of the isolated water molecule at 3657 and 3756 wavenumbers. 8 This is determined by the unique and complex network of intermolecular hydrogen bonds which weakens the intramolecular stretches.…”

Machine Learning for Vibrational Spectroscopy via Divide-and-Conquer Semiclassical Initial Value Representation Molecular Dynamics with Application to N-Methylacetamide

“…For example, a collection of +1 values allows one to enhance the ZPE signal (together with the even transitions), while a selected j = −1 and the remaining i =j = +1 enhances the odd transition of the j − th mode. 43 MC SCIVR has been successfully applied to the study of several systems, [44][45][46][47][48][49][50][51][52][53][54][55] including the different conformers of the glycine amino acid. 44 To improve with respect to the harmonic initial conditions of Eq.s (7), a preliminary adiabatic switching warm up can be implemented with the result that frequency estimates are generally more accurate and complications due to deterministic chaos are largely avoided.…”

Machine learning for vibrational spectroscopy via divide-and-conquer semiclassical initial value representation molecular dynamics with application to N-methylacetamide

“…For example, a collection of +1 values allows one to enhance the zero-point energy (ZPE) signal (together with the even transitions), while a selected ϵj = −1 and the remaining ϵi≠j = +1 enhance the odd transition of the jth mode. 43 The MC SCIVR has been successfully applied to the study of several systems, [44][45][46][47][48][49][50][51][52][53][54][55] including the different conformers of the glycine amino acid. 44 To improve with respect to the harmonic initial conditions of Eq.…”