A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations

To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning. This work is paving the way for using simulated vibrational spectra as a tool to identify materials with defined stoichiometry, enabling the separation of genuine vibrational features of pure phases from morphological and defect-induced signals.

Automated summary

To date, vibrational simulation results have been mainly used as experimental support rather than predictive tools due to the discreteness of the simulated vibrational modes caused by quantization. In this study, we propose a method to combine outputs from ab initio simulations, such as the phonon density of states surrogate and peak intensities, to enable comparison with experimental data using machine learning. This work opens up possibilities for using simulated vibrational spectra to identify materials with defined stoichiometry, allowing for the separation of genuine vibrational features from morphological and defect-induced signals.