Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
Abstract
Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1
Keywords
Cite
@article{arxiv.2506.08423,
title = {Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy},
author = {Utkarsh Pratiush and Austin Houston and Kamyar Barakati and Aditya Raghavan and Dasol Yoon and Harikrishnan KP and Zhaslan Baraissov and Desheng Ma and Samuel S. Welborn and Mikolaj Jakowski and Shawn-Patrick Barhorst and Alexander J. Pattison and Panayotis Manganaris and Sita Sirisha Madugula and Sai Venkata Gayathri Ayyagari and Vishal Kennedy and Ralph Bulanadi and Michelle Wang and Kieran J. Pang and Ian Addison-Smith and Willy Menacho and Horacio V. Guzman and Alexander Kiefer and Nicholas Furth and Nikola L. Kolev and Mikhail Petrov and Viktoriia Liu and Sergey Ilyev and Srikar Rairao and Tommaso Rodani and Ivan Pinto-Huguet and Xuli Chen and Josep Cruañes and Marta Torrens and Jovan Pomar and Fanzhi Su and Pawan Vedanti and Zhiheng Lyu and Xingzhi Wang and Lehan Yao and Amir Taqieddin and Forrest Laskowski and Xiangyu Yin and Yu-Tsun Shao and Benjamin Fein-Ashley and Yi Jiang and Vineet Kumar and Himanshu Mishra and Yogesh Paul and Adib Bazgir and Rama chandra Praneeth Madugula and Yuwen Zhang and Pravan Omprakash and Jian Huang and Eric Montufar-Morales and Vivek Chawla and Harshit Sethi and Jie Huang and Lauri Kurki and Grace Guinan and Addison Salvador and Arman Ter-Petrosyan and Madeline Van Winkle and Steven R. Spurgeon and Ganesh Narasimha and Zijie Wu and Richard Liu and Yongtao Liu and Boris Slautin and Andrew R Lupini and Rama Vasudevan and Gerd Duscher and Sergei V. Kalinin},
journal= {arXiv preprint arXiv:2506.08423},
year = {2025}
}