  The adatom arrays on surfaces offer an ideal playground to explore the
mechanisms of chemical bonding via changes in the local electronic tunneling
spectra. While this information is readily available in hyperspectral scanning
tunneling spectroscopy data, its analysis has been considerably impeded by a
lack of suitable analytical tools. Here we develop a machine learning based
workflow combining supervised feature identification in the spatial domain and
un-supervised clustering in the energy domain to reveal the details of
structure-dependent changes of the electronic structure in adatom arrays on the
Co3Sn2S2 cleaved surface. This approach, in combination with first-principles
calculations, provides insight for using artificial neural networks to detect
adatoms and classifies each based on their local neighborhood comprised of
other adatoms. These structurally classified adatoms are further spectrally
deconvolved. The unexpected inhomogeneity of electronic structures among
adatoms in similar configurations is unveiled using this method, suggesting
there is not a single atomic species of adatoms, but rather multiple types of
adatoms on the Co3Sn2S2 surface. This is further supported by a slight contrast
difference in the images (or slight size variation) of the topography of the
adatoms.
