Scanning tunneling microscopy (STM) is a powerful technique
for
imaging atomic structure and inferring information on local elemental
composition, chemical bonding, and electronic excitations. However,
a plain visual analysis of STM images can be challenging for such
determination in multicomponent alloys, particularly beyond the diluted
limit due to chemical disorder and electronic inhomogeneity. One viable
solution is to use machine learning to analyze STM data and identify
hidden patterns and correlations. Here, we apply this approach to determine the Se/S concentration
in superconducting single-layer FeSe1–xSxalloys epitaxially grown on
SrTiO3(001) substrates via molecular beam epitaxy. First,
the K-means clustering method is applied to identify defect-related
dI/dVtunneling spectra taken by
current imaging tunneling spectroscopy. Then, the Se/S ratio is calculated
by analyzing the remaining spectra based on the singular value decomposition
method. Such analysis provides an efficient and reliable determination
of alloy composition and further reveals the correlations of nanoscale
chemical inhomogeneity to superconductivity in single-layer iron chalcogenide
films.