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The adatom arrays on surfaces offer an ideal playground to explore themechanisms of chemical bonding via changes in the local electronic tunnelingspectra. While this information is readily available in hyperspectral scanningtunneling spectroscopy data, its analysis has been considerably impeded by alack of suitable analytical tools. Here we develop a machine learning basedworkflow combining supervised feature identification in the spatial domain andun-supervised clustering in the energy domain to reveal the details ofstructure-dependent changes of the electronic structure in adatom arrays on theCo3Sn2S2 cleaved surface. This approach, in combination with first-principlescalculations, provides insight for using artificial neural networks to detectadatoms and classifies each based on their local neighborhood comprised ofother adatoms. These structurally classified adatoms are further spectrallydeconvolved. The unexpected inhomogeneity of electronic structures amongadatoms in similar configurations is unveiled using this method, suggestingthere is not a single atomic species of adatoms, but rather multiple types ofadatoms on the Co3Sn2S2 surface. This is further supported by a slight contrastdifference in the images (or slight size variation) of the topography of theadatoms.

Revealing the chemical bonding in adatoms arrays via machine learning of 3D scanning tunneling spectroscopy data