# Decomposition Analysis of Theoretical Raman Spectra for Efficient Interpretation of Experimental Spectra of Thin-Film Functional Materials

## TL;DR

This paper presents a new method for analyzing Raman spectra to better understand the structure of complex thin-film materials.

## Contribution

A novel spectral decomposition approach that preserves molecular interactions for accurate Raman band assignment.

## Key findings

- Decomposing theoretical Raman spectra into atomic group contributions simplifies band assignment.
- The method was successfully applied to mesoporous SBA-15 silica thin films with copper phosphonate groups.
- The approach enhances the correlation between theoretical and experimental Raman data.

## Abstract

This study introduces a novel approach for analyzing theoretical Raman spectra, designed to facilitate spectral interpretation, particularly for complex systems such as functional mesoporous silica-based thin films. The proposed methodology relies on spectral decomposition supported by theoretical calculations, representing a step toward the development of autonomous research laboratories. The method assigns vibrational shifts to individual atoms within a molecular model and uses this information to generate partial spectra corresponding to specific atomic groupings. Unlike separate calculations for isolated components, this approach preserves the mutual interactions within the entire molecular structure, providing a more accurate representation of the vibrational environment. Decomposing the theoretical spectrum into contributions from atomic groups significantly simplifies the assignment of Raman bands to specific structural units, thereby enhancing the interpretative power of theoretical spectra and their correlation with experimental data. The method was demonstrated using real Raman spectroscopic data obtained from mesoporous SBA-15 silica thin films containing copper phosphonate groups. This work also highlights the critical role of molecular modeling and DFT calculations in Raman spectral analysis and outlines future perspectives for the use of artificial intelligence to automate and optimize the spectral interpretation process.