Mini-workshop: Machine learning for experimental condensed matter physics

Workshop Image

Department of Physics, University of Zurich

The aim of this mini-workshop is to familiarize participants with the principles of neural-network-based machine-learning applications in condensed matter physics. Besides the theoretical background, we will also demonstrate elementary examples and the participants will learn how to set up a simple neural network calculation in the google-developed package tensor flow. No previous knowledge is required — except for python, in order to participate in the hands-on programming session. The topics are geared towards data analysis, not so much in the direction of the more fancy applications of neural networks in theoretical condensed matter physics.

Titus Neupert (Slides (PDF, 23 MB))
Lecture I: Basics of neural networks
Lecture II: Overview of neural network applications in condensed matter physics

Kenny Choo and Mark Fischer: Demonstration examples (Notebook)
Eliska Greplova: Flake searching with AI (Slides (PDF, 46 MB)Notebook)
Frank Schindler: hands-on exercise session (Notebook)


For inquiries, please contact