20/07/2021
We are excited to invite you to the virtual school “Machine Learning and Automated Experiment in Scanning Probe Microscopy” to be held at ORNL, October 4-7, 2021, organized by Rama Vasudevan, Kyle Kelley, Maxim Ziatdinov, Josh Agar, and Sergei V. Kalinin. Due to the close similarity between SPM and STEM, these topics may also be of interest for STEM community as well.
The registration deadline is August 25. Link to register:
https://www.surveymonkey.com/r/VDWFV5K
Machine learning (ML) has emerged as a powerful tool for data and image analysis and as an enabling component of autonomous systems in areas ranging from biological and medical imaging to self-driving cars. This rapid growth in ML applications poses the question as to which of these methods can be applied in scanning probe microscopy, both to derive insights into the physics and chemistry of real materials, enable optimization of imaging conditions, and engender transition to the automated and autonomous experiment. This virtual school on ML and AE for SPM, to be held virtually on October 4-7, 2021, will combine 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 will be made on necessary conditions for physically-meaningful machine learning analysis and especially automated experiments in SPM. It will feature tutorials on 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 will be followed by hands-on tutorial sessions introducing the attendees to the AtomAI, GPim, PyroVED, and various Pycroscopy packages. All the technologies and workflows discussed during the tutorials will be open source. The attendees are encouraged to contact the organizers in advance to setup analysis of own datasets. The meeting will be free of charge. The final program will be available by September 2021.
If interested, please contact Sergei Kalinin ([email protected]) to submit the abstract.
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