Benchmarking DFT Accuracy in Predicting O 1s Binding Energies on Metals

TL;DR

This paper evaluates how well DFT predicts oxygen binding energies in metals, finding that accuracy decreases at higher energies.

Contribution

The study provides a benchmark dataset and reveals DFT's limitations in predicting high-binding-energy oxygen species on metals.

Findings

DFT accuracy decreases for oxygen binding energies above ≈530 eV.

Molecularly bound oxygen species are predicted more reliably than atomic ones.

High-binding-energy species like those in Ag-catalyzed epoxidation are poorly represented by DFT.

Abstract

X-ray photoelectron spectroscopy (XPS) is a powerful
tool for probing
the electronic structure and composition of materials, particularly
metals and metal oxides of relevance to solar cells and catalysis.
Density functional theory (DFT) is often used to support XPS peak
assignments, but its reliability for predicting oxygen species is
not well established. Here, we compile a large data set of experimental
oxygen binding energies and evaluate corresponding DFT predictions.
We find that as the binding energies of metal-bound atomic oxygen
species increase, especially above ≈530 eV, there is a general
decrease in the accuracy of DFT-predicted values. Thus, high-binding-energy
atomic oxygen species, such as those proposed as active for selective
Ag-catalyzed epoxidation, are less well represented. The chemical
nature of the oxygen species also influences accuracy, with molecularly
bound species more reliably captured across the entire range of energies.
These findings illustrate the limitations of DFT for interpreting
XPS spectra and provide a benchmark for improving computational methods.

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