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

TL;DR

This paper introduces a machine learning workflow that analyzes hyperspectral scanning tunneling spectroscopy data to reveal detailed electronic structure changes and classify adatoms on a surface, uncovering multiple adatom types.

Contribution

The study develops a combined supervised and unsupervised machine learning approach integrated with first-principles calculations to analyze and classify adatoms based on their electronic and structural features.

Findings

Unveiled inhomogeneity in electronic structures among similar adatoms.

Identified multiple types of adatoms on the Co3Sn2S2 surface.

Demonstrated the effectiveness of machine learning in analyzing hyperspectral STM data.

Abstract

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.