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

作者：Utkarsh Pratiush, Austin Houston, Kamyar Barakati, Aditya Raghavan, Ralph Bulanadi, Xiangyu Yin, Samuel S. Welborn, Dasol Yoon, K. P. Harikrishnan, Zhaslan Baraissov, Desheng Ma, Mikolaj Jakowski, Shawn-Patrick Barhorst, Alexander Pattison, Panayotis Manganaris, Sita Sirisha Madugula, Sai Venkata Gayathri Ayyagari, Vishal Kennedy, Michelle Wang, K. W. Peter Pang, Ian Addison-Smith, Willy Menacho, Horacio V. Guzman, Alexander Kiefer, Nicholas Furth, Nikola L. Kolev, М. П. Петров, Vanessa Liu, Sergey Ilyev, Srikar Rairao, Tommaso Rodani, Ivan Pinto‐Huguet, Xuli Chen, Josep Cruañes, Marta Torrens, Jovan Pomar, Fei Su, Pawan Vedanti, Zhiheng Lyu, Xingzhi Wang, Lehan Yao, Amir Taqieddin, Forrest A. L. Laskowski, Yu‐Tsun Shao, Benjamin Fein-Ashley, Yi Jiang, Vineet Kumar, Himanshu Mishra, Yves Paul, Adib Bazgir, Rama chandra Praneeth Madugula, Yuwen Zhang, Pravan Omprakash, Jian Huang, Eric Montufar-Morales, Vivek Chawla, Harshit Sethi, Jie Huang, Lauri Kurki, Grace Guinan, Addison Salvador, Arman Ter-Petrosyan, Madeline Van Winkle, Steven R. Spurgeon, G. Narasimha, Zijie Wu, Yu Liu, Yongtao Liu, Boris N. Slautin, Andrew R. Lupini, Rama K. Vasudevan, Gerd Duscher, Sergei V. Kalinin · 发表于：Machine Learning Science and Technology · 年份：2025 · DOI：10.1088/2632-2153/ae1f5d · 被引用次数：1 · 研究领域：Biomedical and Engineering Education、vaccines and immunoinformatics approaches、Advanced Electron Microscopy Techniques and Applications

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 micr...