69 citations · 146 across the 7 of their papers we have counts for
9 papers
Operator Forces For Coarse-Grained Molecular Dynamics
Leon Klein, Atharva Kelkar, Aleksander Durumeric +2
Coarse-grained (CG) molecular dynamics simulations extend the length and time scale of atomistic simulations by replacing groups of correlated atoms with CG beads. Machine-learned…
Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
Michael Plainer, Hao Wu, Leon Klein +2
In recent years, diffusion models trained on equilibrium molecular distributions have proven effective for sampling biomolecules. Beyond direct sampling, the score of such a model…
Path Gradients after Flow Matching
Lorenz Vaitl, Leon Klein
Boltzmann Generators have emerged as a promising machine learning tool for generating samples from equilibrium distributions of molecular systems using Normalizing Flows and import…
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…
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde +5
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with…
Temperature Steerable Flows and Boltzmann Generators
Manuel Dibak, Leon Klein, Andreas Krämer +1
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples in thermodynamic eq…
Temperature-steerable flows
Manuel Dibak, Leon Klein, Frank Noé
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical syste…
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noé
Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution…
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies
Jonas Köhler, Leon Klein, Frank Noé
Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest. Boltzmann…