The
rapid growth of methods emerging in the past decade for synthesis
of “designer” catalystsranging from the size
and shape-selected nanoparticles to mass-selected clusters, to precisely
engineered bimetallic surfaces, to single site and pair site catalystshas
opened opportunities for tailoring the catalyst structure for the
desired activity and selectivity. It has also sharpened the need for
developing approaches to the operando characterization, ones that
identify the catalytic active sites and follow their evolutions in
reaction conditions. Commonly used methods for determination of the
activity descriptors in the nanocatalysts, based on the correlation
between the changes in catalyst performance and evolution of its structural
and electronic properties, are hampered by the paucity of experimental
techniques that can detect such properties with high accuracy and
in reaction conditions. Out of many such techniques, X-ray absorption
spectroscopy (XAS) stands out as an element-specific method that is
very sensitive to the local geometric and electronic properties of
the metal atoms and their surroundings and, therefore, is able to
track catalyst structure modifications in operando conditions. Despite
the vast amount of structure-specific information (such as, e.g.,
the charge states and radial distribution function of neighbors of
selected atomic species) stored in the XAS data of catalysts, extracting
it from the spectra is challenging, especially in the conditions of
low metal weight loading, nanoscale dimensions, heterogeneous size
and composition distributions, and harsh reaction environment. In
this Perspective, we discuss the recent developments in XAS data analysis
achieved by employing supervised and unsupervised machine learning
(ML) methods for structural characterization of catalysts. By benefiting
from the sensitivity of ML methods to subtle variations in experimental
data, a previously “hidden” relationship between the
X-ray absorption spectrum and descriptors of material’s structure
and/or composition can be found, as illustrated on representative
examples of mono-, hetero-, and nonmetallic catalysts. In the case
of supervised ML, the experimental spectra can be rapidly “inverted”,
and the structure of the catalyst can be tracked in real time and
in reaction conditions. Emerging opportunities for catalysis research
that the ML methods enable, such as high-throughput data analysis,
and their applications to other experimental probes of catalyst structure
are discussed.