Deciphering Alloy Composition in Superconducting Single-Layer FeSe1–xSx on SrTiO3(001) Substrates by Machine Learning of STM/S Data

Zou, Qiang; Oli, Basu Dev; Zhang, Huimin; Benigno, Joseph; Li, Xin; Li, Lian

doi:10.1021/acsami.2c23324

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

Journal Article · 01 May 2023 · ACS Applied Materials and Interfaces

DOI: https://doi.org/10.1021/acsami.2c23324 · OSTI ID:1972012

^[1]; Oli, Basu Dev ^[1]; Zhang, Huimin ^[1]; Benigno, Joseph ^[1]; Li, Xin ^[1]; Li, Lian ^[1]

West Virginia Univ., Morgantown, WV (United States)

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 identify defect-related dI/dV tunneling 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.

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Research Organization:: West Virginia Univ., Morgantown, WV (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0017632; SC0021393

OSTI ID:: 1972012

Alternate ID(s):: OSTI ID: 1973236

Journal Information:: ACS Applied Materials and Interfaces, Vol. 15, Issue 18; ISSN 1944-8244

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
alloy composition
single-layer iron chalcogenides
MBE
STM/S
machine learning
alloys
imaging
inhomogeneity
scanning tunneling microscopy
tunneling

Title: Deciphering Alloy Composition in Superconducting Single-Layer FeSe1–xSx on SrTiO3(001) Substrates by Machine Learning of STM/S Data

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