Infrared spectroscopy (IR) is a widely used technique enabling to identify specific functional groups in the molecule of interest based on their characteristic vibrational modes or the presence of a specific adsorption site based on the characteristic vibrational mode of an adsorbed probe molecule. The interpretation of an IR spectrum is generally carried out within a fingerprint paradigm by comparing the observed spectral features with the features of known references or theoretical calculations. This work demonstrates a method for extracting quantitative structural information beyond this approach by application of machine learning (ML) algorithms. Taking palladium hydride formation as an example, Pd-H pressure-composition isotherms are reconstructed using IR data collected in situ in diffuse reflectance using CO molecule as a probe. To the best of the knowledge, this is the first example of the determination of continuous structural descriptors (such as interatomic distance and stoichiometric coefficient) from the fine structure of vibrational spectra, which opens new possibilities of using IR spectra for structural analysis.
Contact
Dr Aram Bugaev
Operando spectroscopy group
Swiss Light Source, Paul Scherrer Intitute
5232 Villigen-PSI, Switzerland
Telephone: +41 56 310 32 52
E-mail: aram.bugaev@psi.ch
Original Publication
Machine Learning for Quantitative Structural Information from Infrared Spectra: The Case of Palladium Hydride
Oleg Usoltsev, Andrei Tereshchenko, Alina Skorynina, Elizaveta Kozyr, Alexander Soldatov, Olga Safonova, Adam H. Clark, Davide Ferri, Maarten Nachtegaal, Aram Bugaev*
SMALL Methods, 31 January 204
DOI:10.1002/smtd.202301397