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Machine Learning and Automated Experiments in Scanning Probe Microscopy Virtual School Recordings, October 4-7, 2021

Organizers:  Rama Vasudevan, Kyle Kelley, Maxim Ziatdinov, Josh Agar, and Sergei V. Kalinin

This virtual school on ML and AE in SPM, held October 4-7, 2021, combined invited and contributed presentations at the forefront of ML applications in Scanning Probe Microscopy, including both atomically resolved Scanning Tunneling Microscopy and Spectroscopy and mesoscopic Scanning Probe Microscopy techniques. Special emphasis was made on necessary conditions for physically-meaningful machine learning analysis and especially automated experiments in SPM. Tutorials featured recent developments in ML analysis of mesoscopic and atomically resolved images and spectroscopy, including deep convolutional neural networks (DCNNs) for feature identifications, symmetry-invariant (variational) autoencoders ((V)AE), and Gaussian Processes and Deep Kernel Learning-based super-resolution imaging and image reconstruction, and reinforcement learning for image optimization and automated experiment. The presentations were followed by hands-on tutorial sessions introducing the attendees to the AtomAI, GPim, PyroVED, and various Pycroscopy packages.

The workshop presentations and tutorials (8 videos) are located here.  

The Agenda is located here for reference.