Deciphering Infrared Spectra in TBA-CCl₄ Mixtures via a Hybrid MD-DFT Framework.

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Microscopic molecular clustering dictates complex liquid behavior.
• Overlapping vibrational signatures hinder analysis of specific cluster contributions.

Purpose of the Study:

• Develop a computational framework to reconstruct and analyze infrared (IR) and excess IR spectra.
• Quantitatively link microscopic cluster populations to macroscopic liquid properties.

Main Methods:

• Integrated molecular dynamics (MD) sampling and density functional theory (DFT) calculations.
• Constrained geometry optimization to reconstruct spectra from thousands of cluster isomers.
• Statistical weighting of spectra to match experimental data without prior structural assumptions.

Main Results:

• Successfully reproduced experimental IR and excess IR spectra for TBA-CCl4 mixtures.
• Demonstrated that concentration-dependent IR changes are driven by cluster population shifts, not solely hydrogen bond weakening.
• Identified complex excess IR features as arising from cooperative effects of multiple hydrogen-bonded networks.

Conclusions:

• The computational framework accurately deconvolutes molecular-level heterogeneity in condensed phases.
• Spectroscopic analysis reveals a balance between small oligomer formation and large cluster dissociation in complex liquids.
• This approach bridges atomistic simulations and experimental spectroscopic observables effectively.