Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening Article Swipe
Mark P. Oxley
,
Junqi Yin
,
Nikolay Borodinov
,
Suhas Somnath
,
Maxim Ziatdinov
,
Andrew R. Lupini
,
Stephen Jesse
,
Rama K. Vasudevan
,
Sergei V. Kalinin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.09039
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.09039
Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.
Related Topics
Concepts
Interface (matter)
Focus (optics)
Scanning transmission electron microscopy
Convolutional neural network
Computer science
Diffraction
Transmission electron microscopy
Inverse
Deep learning
Artificial intelligence
Transmission (telecommunications)
Biological system
Materials science
Pattern recognition (psychology)
Physics
Nanotechnology
Optics
Geometry
Mathematics
Biology
Parallel computing
Bubble
Maximum bubble pressure method
Telecommunications
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2002.09039
- https://arxiv.org/pdf/2002.09039
- OA Status
- green
- Cited By
- 1
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3008313742
All OpenAlex metadata
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https://openalex.org/W3008313742Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2002.09039Digital Object Identifier
- Title
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Deep learning of interface structures from the 4D STEM data: cation intermixing vs. rougheningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-02-20Full publication date if available
- Authors
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Mark P. Oxley, Junqi Yin, Nikolay Borodinov, Suhas Somnath, Maxim Ziatdinov, Andrew R. Lupini, Stephen Jesse, Rama K. Vasudevan, Sergei V. KalininList of authors in order
- Landing page
-
https://arxiv.org/abs/2002.09039Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2002.09039Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2002.09039Direct OA link when available
- Concepts
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Interface (matter), Focus (optics), Scanning transmission electron microscopy, Convolutional neural network, Computer science, Diffraction, Transmission electron microscopy, Inverse, Deep learning, Artificial intelligence, Transmission (telecommunications), Biological system, Materials science, Pattern recognition (psychology), Physics, Nanotechnology, Optics, Geometry, Mathematics, Biology, Parallel computing, Bubble, Maximum bubble pressure method, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2020: 1Per-year citation counts (last 5 years)
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21Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2002.09039 |
| primary_location.id | pmh:oai:arXiv.org:2002.09039 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2002.09039 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2002.09039 |
| publication_date | 2020-02-20 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W1987925109, https://openalex.org/W2029599959, https://openalex.org/W2015925980, https://openalex.org/W2066403660, https://openalex.org/W2151425752, https://openalex.org/W1972369518, https://openalex.org/W1987731163, https://openalex.org/W2802656987, https://openalex.org/W2962777415, https://openalex.org/W1982209745, https://openalex.org/W1781107832, https://openalex.org/W2962793481, https://openalex.org/W2134677038, https://openalex.org/W2041958736, https://openalex.org/W2326385550, https://openalex.org/W1990098668, https://openalex.org/W3104499313, https://openalex.org/W2081298453, https://openalex.org/W2078797539, https://openalex.org/W2003652647, https://openalex.org/W1984970784 |
| referenced_works_count | 21 |
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| abstract_inverted_index.4D | 61, 99 |
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| abstract_inverted_index.be | 166 |
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| abstract_inverted_index.is | 38, 130, 162 |
| abstract_inverted_index.it | 129 |
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| abstract_inverted_index.DCNN | 105, 122 |
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| abstract_inverted_index.(STEM) | 66 |
| abstract_inverted_index.LaAlO3 | 82 |
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| abstract_inverted_index.>95% | 133 |
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| abstract_inverted_index.SrTiO3, | 84 |
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| abstract_inverted_index.physics | 14 |
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| abstract_inverted_index.datasets | 67, 101 |
| abstract_inverted_index.electron | 24, 64 |
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| abstract_inverted_index.research, | 15 |
| abstract_inverted_index.simulated | 60, 124 |
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| abstract_inverted_index.chemically | 142 |
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| abstract_inverted_index.interfaces. | 73 |
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| abstract_inverted_index.simulations | 116 |
| abstract_inverted_index.transmission | 23, 63 |
| abstract_inverted_index.convolutional | 54 |
| abstract_inverted_index.determination | 150 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |