Academic Journal
AFM-net: machine learning acceleration of atomic force microscopy nanometrology from scarce data and fast scans.
| Title: | AFM-net: machine learning acceleration of atomic force microscopy nanometrology from scarce data and fast scans. |
|---|---|
| Authors: | Natinsky, Eva, Khan, Ryan M., Wang, Qianshu, Morris, David, Cullinan, Michael, Dingreville, Rémi |
| Source: | NPJ Computational Materials; 6/8/2026, Vol. 12 Issue 1, p1-13, 13p |
| Subject Terms: | Atomic force microscopy, Machine learning, Scanning tunneling microscopy, Data pipelining, Imaging systems, Image reconstruction |
| Abstract: | Atomic Force Microscopy (AFM) is a cornerstone of nanometrology but is constrained by slow acquisition and processing speeds. To address this bottleneck, we introduce AFM-net, a machine learning framework that simultaneously replaces conventional post-processing pipelines and enables high-speed imaging by reconstructing high-fidelity data from fast, low-quality scans. AFM-net leverages a unique training strategy: rather than relying on scarce experimental data, we trained the model on massive natural image datasets overlaid with real, extracted instrumental corruptions. This workflow accelerates data processing by up to 500× and demonstrates zero-shot generalization to other modalities like Scanning Tunneling Microscopy. Ultimately, AFM-net illustrates that overcoming bottlenecks in high-throughput nanometrology depends not only on faster hardware but also on smarter, data-centric computational pipelines. [ABSTRACT FROM AUTHOR] |
| Copyright of NPJ Computational Materials is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1038/s41524-026-02179-7 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20573960&ISBN=&volume=12&issue=1&date=20260608&spage=1&pages=1-13&title=NPJ Computational Materials&atitle=AFM-net%3A%20machine%20learning%20acceleration%20of%20atomic%20force%20microscopy%20nanometrology%20from%20scarce%20data%20and%20fast%20scans.&aulast=Natinsky%2C%20Eva&id=DOI:10.1038/s41524-026-02179-7 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: AFM-net: machine learning acceleration of atomic force microscopy nanometrology from scarce data and fast scans. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Natinsky%2C+Eva%22">Natinsky, Eva</searchLink><br /><searchLink fieldCode="AR" term="%22Khan%2C+Ryan+M%2E%22">Khan, Ryan M.</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qianshu%22">Wang, Qianshu</searchLink><br /><searchLink fieldCode="AR" term="%22Morris%2C+David%22">Morris, David</searchLink><br /><searchLink fieldCode="AR" term="%22Cullinan%2C+Michael%22">Cullinan, Michael</searchLink><br /><searchLink fieldCode="AR" term="%22Dingreville%2C+Rémi%22">Dingreville, Rémi</searchLink> – Name: TitleSource Label: Source Group: Src Data: NPJ Computational Materials; 6/8/2026, Vol. 12 Issue 1, p1-13, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Atomic+force+microscopy%22">Atomic force microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Scanning+tunneling+microscopy%22">Scanning tunneling microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Data+pipelining%22">Data pipelining</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Atomic Force Microscopy (AFM) is a cornerstone of nanometrology but is constrained by slow acquisition and processing speeds. To address this bottleneck, we introduce AFM-net, a machine learning framework that simultaneously replaces conventional post-processing pipelines and enables high-speed imaging by reconstructing high-fidelity data from fast, low-quality scans. AFM-net leverages a unique training strategy: rather than relying on scarce experimental data, we trained the model on massive natural image datasets overlaid with real, extracted instrumental corruptions. This workflow accelerates data processing by up to 500× and demonstrates zero-shot generalization to other modalities like Scanning Tunneling Microscopy. Ultimately, AFM-net illustrates that overcoming bottlenecks in high-throughput nanometrology depends not only on faster hardware but also on smarter, data-centric computational pipelines. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of NPJ Computational Materials is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41524-026-02179-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Atomic force microscopy Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Scanning tunneling microscopy Type: general – SubjectFull: Data pipelining Type: general – SubjectFull: Imaging systems Type: general – SubjectFull: Image reconstruction Type: general Titles: – TitleFull: AFM-net: machine learning acceleration of atomic force microscopy nanometrology from scarce data and fast scans. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Natinsky, Eva – PersonEntity: Name: NameFull: Khan, Ryan M. – PersonEntity: Name: NameFull: Wang, Qianshu – PersonEntity: Name: NameFull: Morris, David – PersonEntity: Name: NameFull: Cullinan, Michael – PersonEntity: Name: NameFull: Dingreville, Rémi IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 06 Text: 6/8/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20573960 Numbering: – Type: volume Value: 12 – Type: issue Value: 1 Titles: – TitleFull: NPJ Computational Materials Type: main |
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