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

arXiv: https://arxiv.org/abs/2506.08423 · HTML: https://mlsanity.com/paper/2506.08423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 124 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Synergizing Human Expertise and AI Efficiency with Language Model for Microscopy Operation and Automated Experiment Design](https://mlsanity.com/paper/2401.13803/) (2024)
- [Autonomous robotic nanofabrication with reinforcement learning](https://mlsanity.com/paper/2002.11952/) (2020)

## Built on by (most cited first, 1 of 1)

- [STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy](https://mlsanity.com/paper/2606.29592/) (2026)

## Abstract (arXiv)

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