Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session M36: Machine Learning in Scanning Probe MicroscopyInvited
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Sponsoring Units: GIMS Chair: Yulia Maximenko, National Institute of Standards and Tech Room: Room 236 |
Wednesday, March 8, 2023 8:00AM - 8:36AM |
M36.00001: TopoStats, a tool to discover the hidden structures and states of biomolecule Invited Speaker: Alice Pyne Atomic Force Microscopy (AFM) can image single molecules in liquid with sub-molecular resolution, without the need for labelling or averaging. Our high-resolution AFM methods can ‘see’ the sub-molecular details of a single biomolecule in liquid, without labelling or averaging, as it ‘explores’ its complex conformational space. This ability is especially important when looking at DNA, a molecule made more complex by its innate flexibility, compaction in the nucleus, and processing by essential cellular enzymes[1]. |
Wednesday, March 8, 2023 8:36AM - 9:12AM |
M36.00002: Machine Learning to classify, predict structure-property relationships, and detect artifacts in AFM images Invited Speaker: Dalia Yablon While machine learning techniques to enhance acquisition and analysis of various microscopy-based techniques is growing quickly, these techniques have been slow to catch on in the scanning probe microscopy community. While there are many reasons for this, one is that SPM suffers from slow acquisition times, making large datasets typically required for ML studies difficult to attain. Despite these challenges, ML is an important tool that can significanatly enhance analysis and interpretation of conventional AFM images. We explore its role here in 3 distinct applications: imaging classification, predicting structure-property relationships, and artifact detection. |
Wednesday, March 8, 2023 9:12AM - 9:48AM |
M36.00003: AI-driven atomic manipulation and characterization in the STEM Invited Speaker: Kevin M Roccapriore Control over matter has allowed physics, condensed matter, and materials science communities to realize a wide range of properties useful in many applications. Both industry and academia have approached the few-nanometer level of control, used largely in the realm of semiconductor fabrication. The scanning transmission electron microscope (STEM), like scanning probe microscopy (SPM), routinely enables direct visualization of the atomic nature of many material systems. Electron-matter interaction in the STEM typically causes undesirable effects that microscopists collectively term “beam damage,” however may be precisely what is needed for atomic fabrication. While manipulation of matter at the level of single atoms has been demonstrated in both STEM and SPM, it has predominantly been performed manually without reproducibility or useful precision. Beam-induced effects in the STEM have been notoriously difficult to control, and the - ordinarily stochastic - changes have also been challenging to characterize analytically via electron energy loss spectroscopy (EELS) or 4D-STEM. |
Wednesday, March 8, 2023 9:48AM - 10:24AM |
M36.00004: Identification of geometrical features of cell surface responsible for cancer aggressiveness: Machine learning analysis of atomic force microscopy images. Invited Speaker: Mikhail Petrov It has been recently demonstrated that atomic force microscopy (AFM) allows for rather precise identification of malignancy of cells. An example of human colorectal epithelial cells imaged in AFM Ringing mode has demonstrated the ability to distinguish cells with different cancer aggressiveness with the help of machine learning (ML). The ML methods traditionally analyze the entire image. The problem with such an approach is the lack of information about which features of the cell surface are associated with the high aggressiveness of the cells. Here we suggest a machine learning approach to overcome this problem and reveal the geometry of features on the cell surface associated with cancer aggressiveness. |
Wednesday, March 8, 2023 10:24AM - 11:00AM |
M36.00005: Universal image segmentation for optical identification of 2D materials Invited Speaker: Randy M Sterbentz Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate. |
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