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Scanning tunneling microscopy (STM) is a powerful technique for imagingatomic structure and inferring information on local elemental composition,chemical bonding, and electronic excitations. However, traditional methods ofvisual inspection can be challenging for such determination in multi-componentalloys, particularly beyond the dilute limit due to chemical disorder andelectronic inhomogeneity. One viable solution is to use machine learning toanalyze STM data and identify patterns and correlations that may not beimmediately apparent through visual inspection alone. Here, we apply thisapproach to determine the Se/S concentration in superconducting single-layerFeSe1-xSx alloy epitaxially grown on SrTiO3(100) substrate by molecular beamepitaxy. First, defect-related dI/dV tunneling spectra are identified by theK-means clustering method, followed by singular value decomposition todistinguish between those from S and Se. Such analysis provides an efficientand reliable determination of local elemental composition, and further revealscorrelations of nanoscale chemical inhomogeneity to superconductivity insingle-layer iron chalcogenide films.

Deciphering alloy composition in superconducting single-layer FeSe1-xSx on SrTiO3(001) substrates by machine learning of STM/S data