Capturing 3D atomic defects and phonon localization at the 2D heterostructure interface
Revealing trimer cluster superstructures at ultrafast timescales in TaTe2
2D vibrational exciton nano-imaging of domain formation in self-assembled monolayer
New phase retrieval methods enabled by the world’s fastest electron detector
The need for rapid and accurate image analysis is increasing in electron microscopy studies of nanomaterials. With newly developed fast, high-efficiency electron detectors and automated imaging protocols, incorporating electron microscopy into high throughput materials design efforts is becoming possible. These new capabilities strongly motivate automated methods to extract relevant structural features, such as nanoparticle size, shape, and defect content, from high resolution transmission electron microscopy (HRTEM) data to link these features to bulk properties and study the influence of heterogeneity on bulk behavior. In general, protocols that surpass the accuracy of traditional image analysis and do not require time-consuming manual analysis are needed. Recent advances in image interpretation using deep learning using machine learning make it a promising route toward automatic interpretation of HRTEM micrographs.
In this STROBE collaboration, we demonstrate a pipeline to detect and classify regions of interest in HRTEM micrographs. Our pipeline uses a convolutional neural net (CNN) to identify crystalline regions (nanoparticles) from an amorphous background in the images, and then feeds individual regions of interest into a random forest classifier to detect whether or not they contain a crystallographic defect. Our CNN has a lightweight U-Net architecture and accurately segments a diverse population of nanoparticles with only a small number of training images. After segmentation, individual nanoparticle regions can be isolated and fed directly into existing python tools to extract size and shape statistics. To detect the presence of defects in nanoparticle regions, we implement a random forest classifier. We demonstrated the random forest classifier’s ability to detect stacking faults in the CdSe subset of identified nanoparticles. Both the CNN and classifier demonstrate state of the art performance at their respective tasks. While this work focuses on HRTEM images of nanoparticles supported on a carbon substrate, in principle the tool can be used to detect any regions of crystallinity in HRTEM data.
Compressive and adaptive nano imaging for enhanced speed and content
Atomic structures determined from digitally defined nanocrystalline regions
STROBE solved a century-old scientific problem: Determining the 3D atomic structure of amorphous solids
Amorphous solids such as glass, plastics and rubber are ubiquitous in our daily life and have broad applications ranging from telecommunications to electronics and solar cells. However, due to the lack of any crystal-like long-range order, the traditional X-ray crystallographic methods for extracting the three-dimensional (3D) atomic arrangement of amorphous solids simply do not work. STROBE advanced atomic electron tomography to determine the 3D atomic positions and chemical species of an amorphous solid for the first time – with a stunning precision of 21 picometer. We found that instead of long-range order characteristic of crystals such as diamond, this amorphous metallic glass had regions of short- and medium-range order. Moreover, although the 3D atomic packing is disordered, some regions connect with each other to form crystal-like networks, which exhibit translational but no orientational order. Looking forward, we anticipate this approach will open the door to determining the 3D atomic coordinates of a wide range of amorphous solids, whose impact on non-crystalline solids may be comparable to the first 3D crystal structure solved by x-ray crystallography over a century ago.