Machine learning has the potential to play a big role in the future of materials discovery and development. Sergei Kalinin, Weston Fulton Professor of materials science and engineering at the University of Tennessee-Knoxville, shares how his development of advanced scanning probe microscopy techniques led to an interest in machine learning and describes some of the benefits, limitations, and challenges of adopting machine learning for materials research.

View the transcript for this episode here.

About the guest
Sergei Kalinin is Weston Fulton Professor of materials science and engineering at the University of Tennessee-Knoxville and chief scientist in artificial intelligence and machine learning for physical sciences at Pacific Northwest National Laboratory. He previously helped develop several advanced scanning probe microscopy techniques when working at Oak Ridge National Laboratory, and he now investigates the use of machine learning methods to improve the technique’s downstream applications for materials discovery and optimization. He taught a course on automated experimentation through the ACerS Online Learning Center in spring 2024, and he will teach another course on scanning probe microscopy this fall.

About ACerS
Founded in 1898, The American Ceramic Society is the leading professional membership organization for scientists, engineers, researchers, manufacturers, plant personnel, educators, and students working with ceramics and related materials.