Bulletin of the American Physical Society
APS March Meeting 2023
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session M36: Machine Learning in Scanning Probe Microscopy
8:00 AM–11:00 AM,
Wednesday, March 8, 2023
Room: Room 236
Sponsoring Unit: GIMS
Chair: Yulia Maximenko, National Institute of Standards and Tech
Abstract: M36.00002 : Machine Learning to classify, predict structure-property relationships, and detect artifacts in AFM images
8:36 AM–9:12 AM
Qualitative (phase imaging) and quantitative (peak force QNM) AFM imaging methods were used to study a series of polymer blends that varied in microstructure and bulk mechanical properties. A deep learning model based on a convolutional neural net (CNN) successfully classified the polymer blends pointing to real and meaningful differences in their microstructure. A separate regression-based CNN was built to correlate the AFM images with various bulk mechanical properties such as Young's modulus, flexural modulus, yield strength, and impact toughness. While the models were successful at predicting the Young’s modulus, flexural modulus, and yield strength, they were unsuccessful at predicting the impact toughness of the material. The success or failure of the deep learning models for this structure-property prediction provide insight into whether morphological or mechanical properties of the microstructure have a stronger influence a particular bulk mechanical properties.
Finally, two approaches were used to detect artifacts in PeakForce QNM images. The first approach used machine learning to successfully classify images that were artifact-free vs. those that had artifacts. The second approach used mathematical calculations of structural similarity to predict “good” or “bad data based on similarities (or lack thereof) between various channels in a single image.
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