AVS Webinar: Automated Experiment in Scanning Probe and Electron Microscopy
As Python-based APIs from major equipment manufacturers are becoming increasingly available, automated experimentation is now accessible to a broad range of scientists in both academia and industry. This webinar is tailored to meet these evolving needs. It is specifically designed to bridge the gap between theoretical data science concepts and their applications in automated microscopy, focusing on developing practical experimental workflows. The course provides an in-depth physicist`s perspective on Bayesian methods and includes introduction into the GPax library.
This approach is aimed at developing both intuition and practical skills necessary for applying Bayesian optimization in real-world experimental settings. Building upon optimization, we will discuss the construction of multistep workflows targeted at discovery of structure-property relationships, identifying physical laws, co-navigation between theory and experimental spaces, and co-orchestration of several tools. By the end of the course, attendees are expected to have a comprehensive understanding of how to apply Bayesian optimization methods effectively for simple tasks such as image optimization, and extend them to real world physics and materials science problems, making this course an invaluable resource for those looking to harness the full potential of ML/AI for modern experimental science.
Webinar Objectives:
- Introduce the general concept of reward-driven workflow design in automated microscopy.
- Provide the basic introduction into Gaussian Processes (GP) and GP-based Bayesian Optimization (BO)
- Introduce structured Gaussian Processes (sGP) and sGP-BO as a framework combining physics discovery and data science.
- Present deep kernel learning (DKL) as an active learning method for structure-property discovery
- Discuss human in the loop automated experiment workflows (hAE) and accelerated microscopy
- Introduce extensions of multi-task and multi-objective GP to design co-orchestration of multiple tools.
- Discuss non-myopic workflows in data analysis and automated experiment.
This webinar is intended for researchers, students, technologists and others involved or interested in using machine learning to enable practically relevant automated experiment workflows in electron and scanning probe microscopy. The webinar will be valuable for a large audience, from young scientists to engineers familiar with microscopies, and assumes no prior knowledge of ML/AI methods.