Deciphering Alloy Composition in Superconducting Single-Layer FeSe1–xSx on SrTiO3(001) Substrates by Machine Learning of STM/S Data
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DOI: 10.1021/acsami.2c23324
Abstract: 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… read more here.
Keywords: superconducting single; composition; machine learning; single layer ... See more keywords