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Deciphering Alloy Composition in Superconducting Single-Layer FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S Data
[Image: see text] 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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176460/ https://www.ncbi.nlm.nih.gov/pubmed/37125966 http://dx.doi.org/10.1021/acsami.2c23324 |
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author | Zou, Qiang Oli, Basu Dev Zhang, Huimin Benigno, Joseph Li, Xin Li, Lian |
author_facet | Zou, Qiang Oli, Basu Dev Zhang, Huimin Benigno, Joseph Li, Xin Li, Lian |
author_sort | Zou, Qiang |
collection | PubMed |
description | [Image: see text] 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–x)S(x) alloys epitaxially grown on SrTiO(3)(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. |
format | Online Article Text |
id | pubmed-10176460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-101764602023-05-13 Deciphering Alloy Composition in Superconducting Single-Layer FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S Data Zou, Qiang Oli, Basu Dev Zhang, Huimin Benigno, Joseph Li, Xin Li, Lian ACS Appl Mater Interfaces [Image: see text] 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–x)S(x) alloys epitaxially grown on SrTiO(3)(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. American Chemical Society 2023-05-01 /pmc/articles/PMC10176460/ /pubmed/37125966 http://dx.doi.org/10.1021/acsami.2c23324 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Zou, Qiang Oli, Basu Dev Zhang, Huimin Benigno, Joseph Li, Xin Li, Lian Deciphering Alloy Composition in Superconducting Single-Layer FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S Data |
title | Deciphering Alloy
Composition in Superconducting Single-Layer
FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S
Data |
title_full | Deciphering Alloy
Composition in Superconducting Single-Layer
FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S
Data |
title_fullStr | Deciphering Alloy
Composition in Superconducting Single-Layer
FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S
Data |
title_full_unstemmed | Deciphering Alloy
Composition in Superconducting Single-Layer
FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S
Data |
title_short | Deciphering Alloy
Composition in Superconducting Single-Layer
FeSe(1–x)S(x) on SrTiO(3)(001) Substrates by Machine Learning of STM/S
Data |
title_sort | deciphering alloy
composition in superconducting single-layer
fese(1–x)s(x) on srtio(3)(001) substrates by machine learning of stm/s
data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176460/ https://www.ncbi.nlm.nih.gov/pubmed/37125966 http://dx.doi.org/10.1021/acsami.2c23324 |
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