Journal Article

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

Mishra, Himanshu, Paul, Yogesh, Narasimha, Ganesh, Barakati, Kamyar, Liu, Viktoriia, Addison-Smith, Ian, Shao, Yu-Tsun, Bazgir, Adib, Su, Fanzhi, Ter-Petrosyan, Arman, Kiefer, Alexander, Kalinin, Sergei V., Ilyev, Sergey, Fein-Ashley, Benjamin, Rodani, Tommaso, Duscher, Gerd, Zhang, Yuwen, Kennedy, Vishal, Huang, Jian, Petrov, Mikhail, Furth, Nicholas, Laskowski, Forrest, Slautin, Boris, Sethi, Harshit, Van Winkle, Madeline, Bulanadi, Ralph, Pattison, Alexander J., Montufar-Morales, Eric, Gayathri Ayyagari, Sai Venkata, Kurki, Lauri, K. P., Harikrishnan, Guzman, Horacio V., Cruañes, Josep, Mang.anaris, Panayotis, Houston, Austin, Torrens, Marta, Praneeth Madugula, Rama Chandra, Pang, Kieran J., Pratiush, Utkarsh, Welborn, Samuel S., Lyu, Zhiheng, Chawla, Vivek, Yoon, Dasol, Ma, Desheng, Lupini, Andrew R., Taqieddin, Amir, Omprakash, Pravan, Liu, Yongtao, Wang, Michelle, Rairao, Srikar, Baraissov, Zhaslan, Kolev, Nikola L., Salvador, Addison, Guinan, Grace, Wang, Xingzhi, Wu, Zijie, Raghavan, Aditya, Yao, Lehan, Vedanti, Pawan, Vasudevan, Rama, Huang, Jie, Jiang, Yi, Spurgeon, Steven R., Madugula, Sita Sirisha

2025

Overview

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).

• Publisher: IOP Publishing
• Subject: electron microscopy / hackathon / machine learning / MATHEMATICS AND COMPUTING / scanning probe microscopy