Improved <i>ab initio</i> molecular dynamics–based vibrational spectroscopy for indirect hard modeling for bulk-phase vibrational spectroscopy using vibrational scaling and sampling diagnostics

Abstract

Ab initio molecular dynamics (AIMD)-derived vibrational spectra provide a promising route toward calibration-free quantitative spectroscopy when combined with indirect hard modeling (IHM). Reliability of AIMD-based spectra for bulk-phase can be compromised by incomplete sampling, electronic-structure errors, and frequency shifts relative to experiment. In this work, an improved AIMD–IHM framework is presented that addresses these limitations through benchmarking comparison of the BLYP and B3LYP/ADMM functional methods to assess their relative accuracy, cluster-resolved sampling analysis based on hydrogen-bond kinetics, and a gas-phase-anchored vibrational frequency scaling strategy. The methodology is demonstrated for aqueous acetic acid, a strongly hydrogen-bonded system characterized by transient molecular associations and proton-sharing motifs. Raman spectra generated from bulk-phase AIMD simulations using BLYP and B3LYP/ADMM are benchmarked against experiment, revealing that the computationally efficient BLYP functional outperforms B3LYP/ADMM in reproducing experimental vibrational frequencies, with a root mean square error (RMSE) of 91 cm−1 compared to 155 cm−1 for the volumetric fraction of 0.2. A region-specific scaling procedure derived from gas-phase data significantly reduces the RMSE of BLYP-based bulk-phase spectra. Hydrogen-bond cluster analysis via reactive-flux indicates that the AIMD trajectories are sufficiently sampled for all relevant molecular motifs and provide quantitative evidence that the spectra are unlikely to be biased by undersampling. When integrated into IHM, the scaled AIMD-derived spectra determine experimental mixture compositions with an RMSE of 0.021 without any experimental calibration, representing an improvement over unscaled spectra, which showed an RMSE of 0.034. The proposed framework enhances the predictive accuracy of AIMD–IHM and extends its applicability to strongly interacting liquid systems.