Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning
Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer a...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-06-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.15.021080 |
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| _version_ | 1849471471853764608 |
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| author | Mads Weile Sergii Grytsiuk Aubrey Penn Daniel G. Chica Xavier Roy Kseniia Mosina Zdenek Sofer Jakob Schiøtz Stig Helveg Malte Rösner Frances M. Ross Julian Klein |
| author_facet | Mads Weile Sergii Grytsiuk Aubrey Penn Daniel G. Chica Xavier Roy Kseniia Mosina Zdenek Sofer Jakob Schiøtz Stig Helveg Malte Rösner Frances M. Ross Julian Klein |
| author_sort | Mads Weile |
| collection | DOAJ |
| description | Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and ab initio calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family. |
| format | Article |
| id | doaj-art-2da49ebab7ad462bb8cdd34a78fcfc33 |
| institution | Kabale University |
| issn | 2160-3308 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj-art-2da49ebab7ad462bb8cdd34a78fcfc332025-08-20T03:24:48ZengAmerican Physical SocietyPhysical Review X2160-33082025-06-0115202108010.1103/PhysRevX.15.021080Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep LearningMads WeileSergii GrytsiukAubrey PennDaniel G. ChicaXavier RoyKseniia MosinaZdenek SoferJakob SchiøtzStig HelvegMalte RösnerFrances M. RossJulian KleinAtomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and ab initio calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family.http://doi.org/10.1103/PhysRevX.15.021080 |
| spellingShingle | Mads Weile Sergii Grytsiuk Aubrey Penn Daniel G. Chica Xavier Roy Kseniia Mosina Zdenek Sofer Jakob Schiøtz Stig Helveg Malte Rösner Frances M. Ross Julian Klein Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning Physical Review X |
| title | Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning |
| title_full | Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning |
| title_fullStr | Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning |
| title_full_unstemmed | Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning |
| title_short | Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning |
| title_sort | defect complexes in crsbr revealed through electron microscopy and deep learning |
| url | http://doi.org/10.1103/PhysRevX.15.021080 |
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