Identifying Adsorption States of OER Intermediates on Single-Atom Catalysts via a Spectral Machine Learning Framework

Identifying the adsorption states of intermediates in
the oxygen
evolution reaction (OER) is crucial for revealing the potential-determining
step and further optimizing catalytic systems. Infrared (IR) spectroscopy
serves as an effective tool for probing oxygen-containing intermediates
on electrode surfaces. However, extracting spectral characteristics
and establishing a quantitative correlation between these features
and the adsorption states of intermediates remains a significant challenge.
In this letter, we present a machine learning framework tailored for
single-atom catalysts to learn from the infrared spectra of OER intermediates
and construct a “spectrum-property” relationship. This
enables accurate prediction of the adsorption states, namely adsorption
free energy and charge of key intermediates (*OH, *O, and *OOH). Notably,
the pretrained model demonstrates efficient transferability across
commonly reported single-atom OER systems and provides interpretable
attention maps of infrared signals based on vibrational mode analysis.
By quantitatively linking spectral features to the adsorption states
of oxygen-containing intermediates via machine learning, our framework
is expected to provide valuable insights for guiding the optimization
of single-atom OER catalysts.