Fig. 2: AI-enhanced TEM analysis framework for self-assembled nanostructures in soft matter.

a Direct visualization and interpretation of organic polymer molecular self-assembly into well-defined nanostructures leveraging imaging¹¹, diffraction¹², and spectroscopy¹³ in TEM. Reproduced with permission from refs. ^11,12,13. Copyright 2023 Springer Nature, 2010 American Chemical Society, and 2021 AAAS. b, c Representative neural network applications in TEM-based morphological, crystallographic, and electronic analysis of self-assembled nanostructures^17,18,19,20. b U-Net¹⁷ architectures are applied across all analysis domains. The data flow for U-Net based segmentation of nanoprisms and nanotubes, showing features obtained at different convolution layers leading to the prediction after training and comparison of thresholds for assessment of connected particle boundaries. c Fully convolutional network (FCN)¹⁸ and convolutional neural network (CNN)¹⁹ are primarily utilized for imaging and diffraction data, while unsupervised deep networks (UDN)²⁰ is adapted for spectroscopy analysis. FCN capable of microtubule annotation in a tomogram. Each neural network trained independently to recognize microtubule¹⁸ (left). CNN predictions on sum of the convolutional layer filters for crystal class, especially hexagonal structure not rhombohedral structure¹⁹ (middle). Unsupervised two-step network of EDS images of quantum dots, followed by a sinogram generation network and FBP and for chemical composition identification and 3D organization²⁰ (right). Reproduced with permission from refs. ^17,18,19,20. Copyright 2020 American Chemical Society and 2017, 2018, and 2021 Springer Nature.