Cover Image

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pratiush, Utkarsh, Houston, Austin, Barakati, Kamyar, Raghavan, Aditya, Yoon, Dasol, KP, Harikrishnan, Baraissov, Zhaslan, Ma, Desheng, Welborn, Samuel S., Jakowski, Mikolaj, Barhorst, Shawn-Patrick, Pattison, Alexander J., Manganaris, Panayotis, Madugula, Sita Sirisha, Ayyagari, Sai Venkata Gayathri, Kennedy, Vishal, Bulanadi, Ralph, Wang, Michelle, Pang, Kieran J., Addison-Smith, Ian, Menacho, Willy, Guzman, Horacio V., Kiefer, Alexander, Furth, Nicholas, Kolev, Nikola L., Petrov, Mikhail, Liu, Viktoriia, Ilyev, Sergey, Rairao, Srikar, Rodani, Tommaso, Pinto-Huguet, Ivan, Chen, Xuli, Cruañes, Josep, Torrens, Marta, Pomar, Jovan, Su, Fanzhi, Vedanti, Pawan, Lyu, Zhiheng, Wang, Xingzhi, Yao, Lehan, Taqieddin, Amir, Laskowski, Forrest, Yin, Xiangyu, Shao, Yu-Tsun, Fein-Ashley, Benjamin, Jiang, Yi, Kumar, Vineet, Mishra, Himanshu, Paul, Yogesh, Bazgir, Adib, Madugula, Rama chandra Praneeth, Zhang, Yuwen, Omprakash, Pravan, Huang, Jian, Montufar-Morales, Eric, Chawla, Vivek, Sethi, Harshit, Huang, Jie, Kurki, Lauri, Guinan, Grace, Salvador, Addison, Ter-Petrosyan, Arman, Van Winkle, Madeline, Spurgeon, Steven R., Narasimha, Ganesh, Wu, Zijie, Liu, Richard, Liu, Yongtao, Slautin, Boris, Lupini, Andrew R, Vasudevan, Rama, Duscher, Gerd, Kalinin, Sergei V.
Format:	Preprint
Published:	2025
Subjects:	Materials Science Machine Learning Instrumentation and Detectors
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866909661430546432
author	Pratiush, Utkarsh Houston, Austin Barakati, Kamyar Raghavan, Aditya Yoon, Dasol KP, Harikrishnan Baraissov, Zhaslan Ma, Desheng Welborn, Samuel S. Jakowski, Mikolaj Barhorst, Shawn-Patrick Pattison, Alexander J. Manganaris, Panayotis Madugula, Sita Sirisha Ayyagari, Sai Venkata Gayathri Kennedy, Vishal Bulanadi, Ralph Wang, Michelle Pang, Kieran J. Addison-Smith, Ian Menacho, Willy Guzman, Horacio V. Kiefer, Alexander Furth, Nicholas Kolev, Nikola L. Petrov, Mikhail Liu, Viktoriia Ilyev, Sergey Rairao, Srikar Rodani, Tommaso Pinto-Huguet, Ivan Chen, Xuli Cruañes, Josep Torrens, Marta Pomar, Jovan Su, Fanzhi Vedanti, Pawan Lyu, Zhiheng Wang, Xingzhi Yao, Lehan Taqieddin, Amir Laskowski, Forrest Yin, Xiangyu Shao, Yu-Tsun Fein-Ashley, Benjamin Jiang, Yi Kumar, Vineet Mishra, Himanshu Paul, Yogesh Bazgir, Adib Madugula, Rama chandra Praneeth Zhang, Yuwen Omprakash, Pravan Huang, Jian Montufar-Morales, Eric Chawla, Vivek Sethi, Harshit Huang, Jie Kurki, Lauri Guinan, Grace Salvador, Addison Ter-Petrosyan, Arman Van Winkle, Madeline Spurgeon, Steven R. Narasimha, Ganesh Wu, Zijie Liu, Richard Liu, Yongtao Slautin, Boris Lupini, Andrew R Vasudevan, Rama Duscher, Gerd Kalinin, Sergei V.
author_facet	Pratiush, Utkarsh Houston, Austin Barakati, Kamyar Raghavan, Aditya Yoon, Dasol KP, Harikrishnan Baraissov, Zhaslan Ma, Desheng Welborn, Samuel S. Jakowski, Mikolaj Barhorst, Shawn-Patrick Pattison, Alexander J. Manganaris, Panayotis Madugula, Sita Sirisha Ayyagari, Sai Venkata Gayathri Kennedy, Vishal Bulanadi, Ralph Wang, Michelle Pang, Kieran J. Addison-Smith, Ian Menacho, Willy Guzman, Horacio V. Kiefer, Alexander Furth, Nicholas Kolev, Nikola L. Petrov, Mikhail Liu, Viktoriia Ilyev, Sergey Rairao, Srikar Rodani, Tommaso Pinto-Huguet, Ivan Chen, Xuli Cruañes, Josep Torrens, Marta Pomar, Jovan Su, Fanzhi Vedanti, Pawan Lyu, Zhiheng Wang, Xingzhi Yao, Lehan Taqieddin, Amir Laskowski, Forrest Yin, Xiangyu Shao, Yu-Tsun Fein-Ashley, Benjamin Jiang, Yi Kumar, Vineet Mishra, Himanshu Paul, Yogesh Bazgir, Adib Madugula, Rama chandra Praneeth Zhang, Yuwen Omprakash, Pravan Huang, Jian Montufar-Morales, Eric Chawla, Vivek Sethi, Harshit Huang, Jie Kurki, Lauri Guinan, Grace Salvador, Addison Ter-Petrosyan, Arman Van Winkle, Madeline Spurgeon, Steven R. Narasimha, Ganesh Wu, Zijie Liu, Richard Liu, Yongtao Slautin, Boris Lupini, Andrew R Vasudevan, Rama Duscher, Gerd Kalinin, Sergei V.
contents	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
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_08423
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy Pratiush, Utkarsh Houston, Austin Barakati, Kamyar Raghavan, Aditya Yoon, Dasol KP, Harikrishnan Baraissov, Zhaslan Ma, Desheng Welborn, Samuel S. Jakowski, Mikolaj Barhorst, Shawn-Patrick Pattison, Alexander J. Manganaris, Panayotis Madugula, Sita Sirisha Ayyagari, Sai Venkata Gayathri Kennedy, Vishal Bulanadi, Ralph Wang, Michelle Pang, Kieran J. Addison-Smith, Ian Menacho, Willy Guzman, Horacio V. Kiefer, Alexander Furth, Nicholas Kolev, Nikola L. Petrov, Mikhail Liu, Viktoriia Ilyev, Sergey Rairao, Srikar Rodani, Tommaso Pinto-Huguet, Ivan Chen, Xuli Cruañes, Josep Torrens, Marta Pomar, Jovan Su, Fanzhi Vedanti, Pawan Lyu, Zhiheng Wang, Xingzhi Yao, Lehan Taqieddin, Amir Laskowski, Forrest Yin, Xiangyu Shao, Yu-Tsun Fein-Ashley, Benjamin Jiang, Yi Kumar, Vineet Mishra, Himanshu Paul, Yogesh Bazgir, Adib Madugula, Rama chandra Praneeth Zhang, Yuwen Omprakash, Pravan Huang, Jian Montufar-Morales, Eric Chawla, Vivek Sethi, Harshit Huang, Jie Kurki, Lauri Guinan, Grace Salvador, Addison Ter-Petrosyan, Arman Van Winkle, Madeline Spurgeon, Steven R. Narasimha, Ganesh Wu, Zijie Liu, Richard Liu, Yongtao Slautin, Boris Lupini, Andrew R Vasudevan, Rama Duscher, Gerd Kalinin, Sergei V. Materials Science Machine Learning Instrumentation and Detectors 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
title	Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
topic	Materials Science Machine Learning Instrumentation and Detectors
url	https://arxiv.org/abs/2506.08423