239 citations · 642 across the 12 of their papers we have counts for
3 papers · 1 filter
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E. Charron, Felix Musil, Andrea Guljas +18
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a uni…
Equivariant flow matching
Leon Klein, Andreas Krämer, Frank Noé
Normalizing flows are a class of deep generative models that are especially interesting for modeling probability distributions in physics, where the exact likelihood of flows allow…
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
Andreas Krämer, Aleksander P. Durumeric, Nicholas E. Charron +3
Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training…