Course Description
Explore machine learning for experiment automation in materials science with no prior ML/AI experience
Discover automated tools for experimental materials and ceramics sciences in this course with Sergei V. Kalinin.
The course will cover topics such as:
- Current opportunities for automated experiment in synthesis and characterization: from single tools to cloud laboratories
- Objectives and rewards for automated experiment
- Reward driven workflow design, orchestration, and execution
- Gaussian processes and Bayesian Optimization
- GP and BO in real world: cost and value
- Bayesian Inference and structured Gaussian Processes
- Hypothesis learning
- Manifold learning, variational autoencoders, and encoders-decoders,
- Deep Kernel Learning and structure-property discovery
- DKL, explainable automated experiments, and human in the loop AE
- Multifidelity structured GP for co-orchestration of multiple tools
- Future perspectives
Course Format
18 hours of instruction
2025 course details coming soon!
Please note that the member registration price for the virtual Short Course is $895.
*Employees of ACerS Corporate Partners receive the discounted Individual Member rate. Sapphire Corporate Partners receive an additional 20% discount; Diamond Corporate Partners receive an additional 30% discount. Please contact Customer Service or 614-890-4700 to register employees at the discounted Corporate Partner rates.
Sergei V. Kalinin, Ph.D.
Sergei V. Kalinin is a Weston Fulton chair professor at the University of Tennessee, Knoxville. In 2022 – 2023, he has been a principal scientist at Amazon (special projects). Before then, he had spent 20 years at Oak Ridge National Laboratory where he was corporate fellow and group leader at the Center for Nanophase Materials Sciences. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002.
His research focuses on the applications of machine learning and artificial intelligence methods in materials synthesis, discovery, and optimization, automated experiment and autonomous imaging and characterization workflows in scanning transmission electron microscopy and scanning probes for applications including physics discovery, atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy.
