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Exploring Nanoworlds: Scanning Probe Microscopy and Machine Learning

Learn the principles, applications, and data analysis techniques in Scanning Probe Microscopy (SPM), with a special focus on ceramic materials

Virtual Short Course: October 15—November 14, 2024

Tuesdays and Thursdays from 11 a.m.–1 p.m. EDT

Course Description

Ceramic materials play a crucial role in various industrial applications from structural ceramics to functional materials such as ferroelectrics, energy storage, and conversion. Functionalities of these materials are almost invariably determined by microstructure, including grain boundaries, topological defects, individual grains, dislocations, and point defects. Understanding these features at the nanoscale is essential for understanding fundamental mechanisms behind materials functionality and hence optimizing their performance.

Scanning Probe Microscopy is a unique and powerful tool capable of exploring materials' structure and functionality from nanometer to macroscopic scales. These techniques allows detailed probing of electronic and ionic transport, ferroelectric polarization dynamics, and electrochemical properties of materials. However, the simplicity of principle is belied by the broad variety of signal formation mechanisms and need for quantitative interpretation of large-dimensional data sets. This course is designed for students, researchers, and industry practitioners who aim to leverage SPM for advanced materials characterization and enhance their data analysis skills through machine learning.

Course Format

 10 sessions, 20 hours of instruction | Virtually via Zoom

The course content will cover:

  • Principles of SPM: Fundamentals of SPM techniques for probing ceramic materials, including conductive Atomic Force Microscopy (AFM), Kelvin Probe Force Microscopy (KPFM) of static and active devices, Piezoresponse Force Microscopy and Electrochemical Strain Microscopy
  • Applications of SPM: Real-world examples showcasing SPM's application in materials and active devices characterization.
    Introduction to machine learning methods for data interpretation, covering linear and non-linear dimensionality reduction, supervised ML methods, and decision making
  • Fundamentals of shallow and deep neural networks for image segmentation, autoencoders, and building structure-property relationships
  • Using SPM as a part of materials discovery and optimization workflows
  • Hands-on machine learning exercises to explore real-world scenarios for spectroscopy and image analysis in SPM of functional ceramics

Instructor Bio

2024 Sergei Kalinin Updated Headshot

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.

Registration Rates

Nonmember Access: $1,100 • Member Access: $995

Customized training

Looking for training customized to your company? Do you want a course taught privately to your employees? Call Customer Service at 614-890-4700 for details, or contact Marcus Fish to learn about training benefits for our Corporate Partners.

*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.

For other questions about ACerS online courses, email

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