Unraveling the Temperature Effects on the Raman Spectra of V₂O₅ by Combining Machine Learning and Molecular Dynamics

Area of Science:

• Materials Science
• Computational Chemistry
• Spectroscopy

Background:

• Raman spectroscopy is crucial for analyzing catalyst atomic structures under working conditions.
• Interpreting operando Raman spectra often necessitates computational support.
• Vanadium pentoxide (V2O5) is a key catalyst with complex temperature-dependent spectral behavior.

Purpose of the Study:

• To investigate the anomalous blue shift observed in a V2O5 Raman mode near 400 cm-1 with increasing temperature.
• To develop and validate a computational framework for interpreting operando Raman spectra.
• To elucidate the atomic-level mechanisms behind the temperature-dependent Raman response of V2O5.

Main Methods:

• Development of a novel Raman calculation method integrating graph neural network machine learning and molecular dynamics.
• Experimental acquisition of Raman spectra for V2O5 under varying temperatures.
• Computational reproduction of experimental Raman spectra to identify spectral origins.

Main Results:

• The developed computational method accurately reproduced experimental Raman spectra of V2O5.
• The anomalous blue shift was attributed to local contraction of a V-V interatomic distance.
• Anisotropic thermal expansion was identified as the underlying cause of the V-V bond contraction.

Conclusions:

• The study provides mechanistic insights into the temperature-dependent Raman response of V2O5 catalysts.
• A robust computational framework for analyzing operando Raman spectra of functional materials was established.
• This approach facilitates a deeper understanding of catalyst behavior under realistic operating conditions.