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

TL;DR

Mic-hackathon 2024 facilitated collaboration between machine learning and microscopy communities, resulting in benchmark datasets, digital twins, and code to advance standardized workflows and real-time analytics in electron and scanning probe microscopy.

Contribution

The hackathon created benchmark datasets, digital twins, and open-source code to bridge the gap between ML and microscopy communities, promoting standardized workflows and real-time analytics.

Findings

Development of benchmark datasets for microscopy

Creation of digital twins of microscopes

Open-source code for ML integration in 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