发表机构
University of Castilla-La Mancha; Guangdong Technion – Israel Institute of Technology; Area Science Park; The University of York; Regional Institute for Applied Scientific Research (IRICA)（卡斯蒂利亚-拉曼恰大学; 广东以色列理工学院; 区域科学园; 约克大学; 区域应用科学研究所（IRICA））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
综述纳米颗粒电子显微镜中人工智能从图像解释到科学推理的演变，围绕颗粒表征挑战，回顾从传统机器学习到多种先进方法，探讨整合多方面数据，评估现有方法利弊，强调其在下一代纳米颗粒表征及加速材料发现中的作用。
AI 中文摘要
人工智能正在通过对越来越大且复杂的纳米颗粒表征数据集进行定量分析来改变电子显微镜。机器学习和深度学习的进展将显微镜从描述性成像技术扩展为数据驱动的结构解释、动态分析和科学推理平台。本文综述了纳米颗粒电子显微镜的人工智能方法，聚焦于透射电子显微镜等多种类型。讨论围绕纳米颗粒表征的主要挑战展开，回顾了从传统机器学习到多种先进计算方法。还探讨了整合显微镜数据与模拟等内容，评估了现有方法的优缺点等，最后讨论了新兴机遇。通过整合多领域进展，强调了人工智能在下一代纳米颗粒表征和加速材料发现中的作用。
英文摘要
Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization. Recent advances in machine learning (ML) and deep learning (DL) have expanded microscopy from a descriptive imaging technique into a data-driven platform for structural interpretation, dynamic analysis, and scientific inference. This review examines AI methodologies for nanoparticle electron microscopy, focusing on transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), and in situ TEM. The discussion is organized around the principal challenges in nanoparticle characterization, including particle detection, segmentation, morphology quantification, atomic-resolution restoration, defect identification, two-dimensional-to-three-dimensional structural inference, and analysis of dynamic processes in situ. We review computational approaches from conventional ML and convolutional neural networks to transformer architectures, self-supervised learning, foundation models, multimodal AI, and physics-informed learning. We further discuss integrating microscopy data with simulations, metadata, and autonomous experimentation to relate nanoparticle structure, dynamics, synthesis conditions, and functional properties. The advantages, limitations, benchmarking, and data requirements of current methodologies are critically assessed. Finally, emerging opportunities for foundation models, AI-guided microscopy, closed-loop experimentation, and autonomous materials discovery are discussed. By integrating advances across computer vision, materials informatics, and electron microscopy, this review highlights the role of AI in next-generation nanoparticle characterization and accelerated materials discovery.
CommentsMain article: 34 pages, 8 figures (including Graphical Abstract), 5 tables. Appendix: 18 pages, 1 figure, 4 tables. This manuscript has been submitted (after invitation) to Advanced Intelligent Discovery for peer review
