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

Elucidating the microscopic clustering of molecules is pivotal to understanding the macroscopic behavior of complex liquids. However, resolving specific cluster contributions from congested vibrational signatures remains a formidable challenge due to severe spectral overlap. Herein, we present a high-throughput computational framework that integrates molecular dynamics sampling and density functional theory calculations with constrained geometry optimization to reconstruct infrared (IR) and excess IR spectra. By statistically weighting the spectra of thousands of cluster isomers, this method quantitatively reproduces experimental measurements without relying on a priori structural assumptions. Applied to TBA-CCl₄ mixtures, it shows that the concentration-dependent IR changes are governed by the redistribution of cluster populations rather than simple hydrogen-bond weakening. Experimentally, this evolution is reflected mainly in band-shape changes and the gradual emergence of a high-frequency shoulder. Furthermore, we demonstrate that the complex features in the excess IR spectra arise not from a single dominant species but from the cooperative contributions of multiple coexisting hydrogen-bonded networks, specifically the competitive balance between the formation of small chain oligomers and the dissociation of large size clusters. This spectroscopically driven strategy provides a robust tool for deconvoluting molecular-level heterogeneity in complex condensed phases, effectively bridging the gap between atomistic simulations and spectroscopic observables.