| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
| Presentation Title |
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy |
| Author(s) |
Sergei Kalinin |
| On-Site Speaker (Planned) |
Sergei Kalinin |
| Abstract Scope |
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research. the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in imaging. In this presentation, I will discuss challenges and opportunities of AE in SPM and STEM, ranging from feature discovery to controlled intervention. The special emphasis is made on the rotationally invariant variational autoencoders that allow to disentangle rotational degrees of freedom from other latent variables in imaging and spectral data. Extension of encoder approach towards establishing structure-property relationships will be illustrated on the example of ferroelectric domain walls and plasmonic structures. I further discuss the strategies based on Gaussian Processes for automated experiment, and demonstrate some initial results for AE in SPM and STEM. |