6 papers
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Winfried Ripken, Michael Plainer, Gregor Lied +5
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a f…
Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Yu Xie, Ludwig Winkler, Lixin Sun +10
The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioE…
Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
Nicholas Gao, Till Grutschus, Frank Noé +1
Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient…
Boltzmann Generators for Condensed Matter via Riemannian Flow Matching
Emil Hoffmann, Maximilian Schebek, Leon Klein +2
Sampling equilibrium distributions is fundamental to statistical mechanics. While flow matching has emerged as scalable state-of-the-art paradigm for generative modeling, its poten…
Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
P. Bernát Szabó, Zeno Schätzle, Frank Noé
A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated…
Assessing generative modeling approaches for free energy estimates in condensed matter
Maximilian Schebek, Jiajun He, Emil Hoffmann +3
The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multi…