Nanoparticles and electron microscopy

We use machine learning methods for analysis related to high-resolution transmission electron microscopy (HR-TEM), and for modelling the dynamics and structure of nanoparticles and related materials. In particular, we work with:

• Deep learning for image analysis for HR-TEM
• Deep learning for exit wave reconstruction for HR-TEM
• Large scale molecular dynamics of materials and nanoparticles
• Equivariant neural network potentials for molecular dynamics

Links to our open source codes:

Atomic Simulation Environment (ASE)

Massively parallel molecular dynamics (ASAP) 

The GPAW electronic structure code

Publications from the group can be found on ORCID.