Welcome to the FAST group!

We are a group of computational scientists fascinated by the study of challenging materials, mostly involving water, by means of computational methods. In recent time, we primarily focus on developing machine learning potentials in order to provide insight into complex aqueous systems, for which accurate and efficient representations of potential energy surfaces are urgently needed.

Our research portfolio comprises the following techniques:

  • Machine Learning Potentials, Neural Network Potentials, Active Learning
  • Ab Initio Molecular Dynamics, Density Functional Theory
  • Nuclear Quantum Effects, Path Integral Molecular Dynamics and Monte Carlo, Bosonic Exchange
  • Reaching Coupled Cluster Accuracy

Find out more about our research under Research.