DFT meets Bayesian inference: creating a framework for the assignment of calculated vibrational frequencies

Abstract

Volatile Organic Compounds (VOCs) are abundant in nature and play vital roles in industries such as food, fragrance, and pharmaceuticals. Aromatic VOCs like vanillin are especially valuable, driving research into sustainable chemical processes, including the conversion of biomass into high-value chemicals. Understanding the molecular structure and vibrational behavior of these compounds is essential for designing and optimising such processes. In this work, we explore how computational modelling can be used to predict and interpret vibrational spectra of VOCs. We also introduce a statistical approach using Bayesian inference to improve how theoretical predictions are matched to experimental observations. This combined strategy enhances the reliability and clarity of spectral interpretation, offering a more consistent framework for studying complex organic molecules.