Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Abstract Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub( https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1 ) and Zenodo ( https://zenodo.org/records/15579940 ).

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Publication Details

Journal
Machine Learning Science and Technology
Published
2025-12-02
DOI
https://doi.org/10.1088/2632-2153/ae1f5d
Primary Topic
Biomedical and Engineering Education
Type
article
Field-Weighted Citation Impact
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article

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Madeline Van Winkle, Aditya Raghavan, Rama Krishnan Vasudevan, Ian Addison-Smith et al.
Machine Learning Science and Technology
Biomedical and Engineering Education
article

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Madeline Van Winkle, Aditya Raghavan, Rama Krishnan Vasudevan, Ian Addison-Smith, Yi Jiang, Zijie Wu, Pravan Omprakash, Vineet Kumar, Dasol Yoon, Steven R. Spurgeon, Adib Bazgir, Zhaslan Baraissov, Xuli Chen, Forrest A. L. Laskowski, Zhiheng Lyu, Marta Torrens, Jie Huang, Amir Taqieddin, Vivek Chawla, Kamyar Barakati, Desheng Ma, Sergei V. Kalinin, John Vedamuthu Kennedy, Yongtao Liu, Austin Houston, Lauri Kurki, Gerd Duscher, Vanessa Liu, Lehan Yao, Boris N. Slautin, G. Narasimha, Willy Menacho, K. P. Harikrishnan, K. W. Peter Pang, Ivan Pinto‐Huguet, Tommaso Rodani, Utkarsh Pratiush, М. П. Петров, Yves Paul, Alexander Kiefer, Sai Venkata Gayathri Ayyagari, Andrew R. Lupini, Samuel S. Welborn, Horacio V. Guzman, Yu‐Tsun Shao, Ralph Bulanadi, Arman Ter-Petrosyan, Yuwen Zhang, Yu Liu, Michelle Wang, Jian Huang, Fei Su, Xiangyu Yin, Himanshu Mishra, Addison Salvador, Alexander Pattison, Sita Sirisha Madugula, Grace Guinan, Benjamin Fein-Ashley, Rama chandra Praneeth Madugula, Shawn-Patrick Barhorst, Nikola L. Kolev, Jovan Pomar, Mikolaj Jakowski, Nicholas Furth, Sergey Ilyev, Srikar Rairao, Pawan Vedanti, Eric Montufar-Morales, Panayotis Manganaris, Josep Cruañes, Harshit Sethi, Xingzhi Wang
article en

Abstract

Abstract Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub( https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1 ) and Zenodo ( https://zenodo.org/records/15579940 ).

Machine Learning Science and TechnologyVol. 6(4)
Universidad de Santiago de Chile (CL), University of Geneva (CH), University of Southern California (US), Tufts University (US), Argonne National Laboratory (US), Oak Ridge National Laboratory (US), National Laboratory of the Rockies (US), Pennsylvania State University (US), North Carolina State University (US), University of Trieste (IT), Pacific Northwest National Laboratory (US), Lawrence Berkeley National Laboratory (US), Moscow Institute of Physics and Technology (RU), University of Illinois Urbana-Champaign (US), Colorado School of Mines (US), University of Colorado Boulder (US), Justus-Liebig-Universität Gießen (DE), University of California, Irvine (US), Washington University in St. Louis (US), Cornell University (US), Charles University (CZ), University of Cambridge (GB), AREA Science Park (IT), Centre de Recerca Matemàtica (ES), Applied Spectra (United States) (US), Institut de Ciència de Materials de Barcelona (ES), Solid Power (United States) (US), Hertie Institute for Clinical Brain Research (DE), National Energy Research Scientific Computing Center (US), University College London (GB), New York University (US), University of Duisburg-Essen (DE), University of Cincinnati (US), University of Chile (CL), University of Tennessee at Knoxville (US), University of Missouri (US), University of Pennsylvania (US), University of Tübingen (DE), Technical University of Denmark (DK), Aalto University (FI)
Openalex Percentile: Top 63%
Biomedical and Engineering Education
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