The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
2026-07-11 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors review how artificial intelligence (AI) is helping scientists better understand nanoparticles using different kinds of electron microscopes. They explain AI methods that find and measure particles, restore detailed images, identify defects, and even watch particles change over time. The review covers many AI approaches, from classic machine learning to newer techniques like transformer models and self-learning. The authors also discuss how AI can combine microscope data with simulations and experiments to connect particle structure and properties. They highlight both the current strengths and challenges in using AI to speed up materials research.
Artificial IntelligenceElectron MicroscopyNanoparticle CharacterizationMachine LearningDeep LearningTransmission Electron Microscopy (TEM)Image SegmentationTransformer ArchitecturesSelf-Supervised LearningMaterials Discovery
Authors
Evropi Toulkeridou, Jiafei Li, Leonardo Lari, Panagiotis Grammatikopoulos
Abstract
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.