Deciphering Alloy Composition in Superconducting Single-Layer FeSe_1-xS_x on SrTiO₃(001) Substrates by Machine Learning of STM/S Data.

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 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 FeSe_1-xS_x alloys epitaxially grown on SrTiO₃(001) substrates via molecular beam epitaxy. First, the K-means clustering method is applied to i

SUBMITTER: Zou Q 

PROVIDER: S-EPMC10176460 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature