Publications

A deep learning-based convolutional neural network has been applied to denoise atomic-resolution in situ transmission electron microscopy (TEM) image datasets of catalyst nanoparticles acquired on high speed, direct electron counting detectors, where the signal is severely limited by shot noise. We leverage multislice TEM image simulation to generate a large and flexible dataset for training and testing the network, and then we apply it to real experimental images of model catalyst consisting of CeO2-supported Pt nanoparticles.

1. Joshua L Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y Sheth, Eero P Simoncelli, David S Matteson, Carlos Fernandez-Granda, Peter A Crozier (2021).
Developing a Deep Neural Network to Denoise Time-Resolved In Situ ETEM Movies of Catalyst Nanoparticles
arxiv.org

2. James Losey, Michael Jauch, David S Matteson, Mahmoud Moradi (2021).
“Simulating freely-diffusing single-molecule FRET data with consideration of protein conformational dynamics”
biorxiv.org

3. Sreyas Mohan, Ramon Manzorro, Joshua L Vincent, Binh Tang, Dev Yashpal Sheth, Eero P Simoncelli, David S Matteson, Peter A Crozier, Carlos Fernandez-Granda (2020).
“Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy”
arxiv.org

4. Roberto Rivera, Janet Rosenbaum, Walter Quispe. (2020). Excess mortality in the United States during the first three months of the COVID-19 pandemic“. Epidemiology and Infection. 148, e264

5. Yanxon H., Zagaceta D., Tang B., Matteson D., Zhu Q. (2020). PyXtal FF: a Python Library for Automated Force Field Generation. Mach. Learn.: Sci. Technol. 2, 027001 (arXiv: PDF)

6. Yanxon H., Zagaceta D., Wood B.C., Zhu Q. (2020). Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon. J. Chem. Phys. 153, 054118 (arXiv: PDF)

7. Zagaceta D., Yanxon H., Zhu Q. (2020). Spectral Neural Network Potentials for Binary Alloys. J. Appl. Phys., 128, 045113. (arXiv: PDF)

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