打造显微镜数据基准集与数字孪生，推动机器学习在材料表征中应用
Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
- 构建显微镜数据基准集与数字孪生系统，支持标准化分析流程
- 发布开源代码与数据，提升跨学科协作效率
- 适合材料科学、机器学习及仪器开发研究人员参与
显微技术是纳米与原子尺度材料结构与功能信息的主要来源，其数据通常结构清晰、带有元数据和样品历史记录，但格式与细节不一致。主要资助机构推行数据管理计划（DMP），促进数据保存与共享，但因缺乏标准化的代码生态、评估基准与集成策略，数据利用效率低，分析耗时长。除后处理分析外，主流显微镜厂商新推出的API支持实时机器学习分析，实现自动化决策与智能控制。然而，机器学习与显微学社区间仍存在鸿沟，限制了这些方法在物理、材料发现与优化中的影响。本次黑客松通过跨学科协作，推动机器学习在显微学中的落地，产出基准数据集与显微镜数字孪生模型，建立标准化工作流。所有代码已公开于GitHub：https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1。
原文摘要 · Abstract (English)
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
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