Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis.

Area of Science:

• Materials Science
• Spectroscopy
• Computational Chemistry

Background:

• Vibrational spectroscopy is crucial for analyzing atomic structures but interpreting spectra is challenging.
• Human analysis of spectroscopic peaks can be difficult and convoluted for complex structures.

Purpose of the Study:

• To develop a reliable protocol using supervised manifold learning to connect vibrational spectra with diverse atomic structure configurations.
• To overcome limitations of classical peak analysis in vibrational spectroscopy.

Main Methods:

• Utilized supervised manifold learning techniques, including linear and nonlinear dimensionality reduction.
• Applied decision trees to correlate reduced spectral features with structural information.
• Generated and analyzed a large database of virtual vibrational spectroscopy profiles from atomistic simulations of silicon.

Main Results:

• Achieved over 97% accuracy in disentangling contributions from different material states (stress, amorphization, disorder).
• Demonstrated robustness against noise in spectroscopic data.
• Successfully correlated spectral features with structural information not discernible through classical peak analysis.

Conclusions:

• The developed protocol offers a comprehensive decoding of vibrational spectroscopic profiles, extending beyond human-identifiable peak analysis.
• Supervised manifold learning provides a powerful approach for complex materials characterization using vibrational spectroscopy.
• This method enhances the ability to link spectroscopic data to detailed atomic structure configurations.