IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-40339-1.html
   My bibliography  Save this article

Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

Author

Listed:
  • Saugat Kandel

    (Argonne National Laboratory)

  • Tao Zhou

    (Argonne National Laboratory)

  • Anakha V. Babu

    (KLA Corporation)

  • Zichao Di

    (Argonne National Laboratory)

  • Xinxin Li

    (Argonne National Laboratory
    University of Chicago)

  • Xuedan Ma

    (Argonne National Laboratory
    University of Chicago)

  • Martin Holt

    (Argonne National Laboratory)

  • Antonino Miceli

    (Argonne National Laboratory)

  • Charudatta Phatak

    (Argonne National Laboratory)

  • Mathew J. Cherukara

    (Argonne National Laboratory)

Abstract

Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe2 film. Our studies show that a FAST scan of

Suggested Citation

  • Saugat Kandel & Tao Zhou & Anakha V. Babu & Zichao Di & Xinxin Li & Xuedan Ma & Martin Holt & Antonino Miceli & Charudatta Phatak & Mathew J. Cherukara, 2023. "Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40339-1
    DOI: 10.1038/s41467-023-40339-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-40339-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-40339-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Benjamin Burger & Phillip M. Maffettone & Vladimir V. Gusev & Catherine M. Aitchison & Yang Bai & Xiaoyan Wang & Xiaobo Li & Ben M. Alston & Buyi Li & Rob Clowes & Nicola Rankin & Brandon Harris & Rei, 2020. "A mobile robotic chemist," Nature, Nature, vol. 583(7815), pages 237-241, July.
    2. S. B. Damelin & N. S. Hoang, 2018. "On Surface Completion and Image Inpainting by Biharmonic Functions: Numerical Aspects," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2018, pages 1-8, February.
    3. Mirko Holler & Manuel Guizar-Sicairos & Esther H. R. Tsai & Roberto Dinapoli & Elisabeth Müller & Oliver Bunk & Jörg Raabe & Gabriel Aeppli, 2017. "High-resolution non-destructive three-dimensional imaging of integrated circuits," Nature, Nature, vol. 543(7645), pages 402-406, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Susan Erikson, 2021. "COVID‐Apps: Misdirecting Public Health Attention in a Pandemic," Global Policy, London School of Economics and Political Science, vol. 12(S6), pages 97-100, July.
    2. Yifan Xie & Shuo Feng & Linxiao Deng & Aoran Cai & Liyu Gan & Zifan Jiang & Peng Yang & Guilin Ye & Zaiqing Liu & Li Wen & Qing Zhu & Wanjun Zhang & Zhanpeng Zhang & Jiahe Li & Zeyu Feng & Chutian Zha, 2023. "Inverse design of chiral functional films by a robotic AI-guided system," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Amanda A. Volk & Robert W. Epps & Daniel T. Yonemoto & Benjamin S. Masters & Felix N. Castellano & Kristofer G. Reyes & Milad Abolhasani, 2023. "AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Jiyu Cui & Fang Wu & Wen Zhang & Lifeng Yang & Jianbo Hu & Yin Fang & Peng Ye & Qiang Zhang & Xian Suo & Yiming Mo & Xili Cui & Huajun Chen & Huabin Xing, 2023. "Direct prediction of gas adsorption via spatial atom interaction learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Miaoqi Chu & Zhang Jiang & Michael Wojcik & Tao Sun & Michael Sprung & Jin Wang, 2023. "Probing three-dimensional mesoscopic interfacial structures in a single view using multibeam X-ray coherent surface scattering and holography imaging," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    6. Zi-Jing Zhang & Shu-Wen Li & João C. A. Oliveira & Yanjun Li & Xinran Chen & Shuo-Qing Zhang & Li-Cheng Xu & Torben Rogge & Xin Hong & Lutz Ackermann, 2023. "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    7. Bin Ouyang & Yan Zeng, 2024. "The rise of high-entropy battery materials," Nature Communications, Nature, vol. 15(1), pages 1-5, December.
    8. Wenhao Gao & Priyanka Raghavan & Connor W. Coley, 2022. "Autonomous platforms for data-driven organic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    9. Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    10. Benjamin P. MacLeod & Fraser G. L. Parlane & Connor C. Rupnow & Kevan E. Dettelbach & Michael S. Elliott & Thomas D. Morrissey & Ted H. Haley & Oleksii Proskurin & Michael B. Rooney & Nina Taherimakhs, 2022. "A self-driving laboratory advances the Pareto front for material properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    11. Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    12. Carles Bosch & Tobias Ackels & Alexandra Pacureanu & Yuxin Zhang & Christopher J. Peddie & Manuel Berning & Norman Rzepka & Marie-Christine Zdora & Isabell Whiteley & Malte Storm & Anne Bonnin & Chris, 2022. "Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    13. Hongyuan Sheng & Jingwen Sun & Oliver Rodríguez & Benjamin B. Hoar & Weitong Zhang & Danlei Xiang & Tianhua Tang & Avijit Hazra & Daniel S. Min & Abigail G. Doyle & Matthew S. Sigman & Cyrille Costent, 2024. "Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    14. Zong-Lin Li & Kun Chen & Fei Li & Zhi-Jun Shi & Qi-Li Sun & Peng-Qi Li & Yu-Gui Peng & Lai-Xin Huang & Guang Yang & Hairong Zheng & Xue-Feng Zhu, 2023. "Decorated bacteria-cellulose ultrasonic metasurface," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    15. Jan Durrer & Prajwal Agrawal & Ali Ozgul & Stephan C. F. Neuhauss & Nitesh Nama & Daniel Ahmed, 2022. "A robot-assisted acoustofluidic end effector," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    16. Anakha V. Babu & Tao Zhou & Saugat Kandel & Tekin Bicer & Zhengchun Liu & William Judge & Daniel J. Ching & Yi Jiang & Sinisa Veseli & Steven Henke & Ryan Chard & Yudong Yao & Ekaterina Sirazitdinova , 2023. "Deep learning at the edge enables real-time streaming ptychographic imaging," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40339-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.