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

References in corpus (11)

• A Survey of Deep Learning Techniques for Autonomous Driving
• Resonant Inelastic X-ray Scattering Studies of Elementary Excitations
• py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets
• Suppression of Octahedral Tilts and Associated Changes of Electronic Properties at Epitaxial Oxide Heterostructure Interfaces
• Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
• Autonomous robotic nanofabrication with reinforcement learning
• Precise atom manipulation through deep reinforcement learning
• Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy
• A Fast Algorithm for Scanning Transmission Electron Microscopy (STEM) Imaging and 4D-STEM Diffraction Simulations
• Benchmark tests of atom segmentation deep learning models with a consistent dataset
• A Foundation Model for Cell Segmentation