NordTEMhub/ARTEMI Workshop Explores Machine Learning in Transmission Electron Microscopy

Linköping University hosted the NordTEMhub/ARTEMI workshop on Machine Learning Methods in Transmission Electron Microscopy, a two-day event focused on the growing role of AI-driven methods in electron microscopy.

The workshop featured talks by the leading experts in the field. Michael Felsberg (LiU) introduced key concepts in machine learning, while Mitra Taheri (Johns Hopkins University) demonstrated its applications in spectroscopy and shared insights from her research on 2D material materials. Antonius van Helvoort (NTNU) discussed the use of machine learning in scanning precession electron diffraction, highlighting its potential to advance materials science.

On the second day, presentations by Jakob Schiøtz (DTU), Ivan Lobato (Rosalind Franklin Institute), and Maria K. Chan (Argonne National Laboratory) explored topics like denoising, particle identification, and simulation methods. These talks provided practical insights into solving common challenges in electron microscopy using machine learning techniques.

Afternoon hands-on sessions gave participants the chance to apply what they learned, offering practical guidance on implementing machine learning scripts in their own research.
The workshop also provided a valuable opportunity for networking and exchanging ideas with others working on cutting-edge projects in the field.

Thanks to NordTEMhub and ARTEMI’s support, the event brought together experts and researchers for a collaborative exploration of how machine learning can enhance transmission electron microscopy.