Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

no code implementations • 4 Apr 2023 • Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, A. Al-Najjar, Christina Doty, Sarah Akers, Nageswara S. Rao, Joshua C. Agar, Steven R. Spurgeon

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy.

Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy

no code implementations • 23 May 2022 • Wenkai Fu, Steven R. Spurgeon, Chongmin Wang, Yuyan Shao, Wei Wang, Amra Peles

We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method.

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

no code implementations • 30 Sep 2021 • Matthew Olszta, Derek Hopkins, Kevin R. Fiedler, Marjolein Oostrom, Sarah Akers, Steven R. Spurgeon

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine.

Decision Making

Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

1 code implementation • 21 Jul 2021 • Christina Doty, Shaun Gallagher, Wenqi Cui, Wenya Chen, Shweta Bhushan, Marjolein Oostrom, Sarah Akers, Steven R. Spurgeon

The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis.

Few-Shot Learning Image Segmentation +1