6 papers
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…
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…
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…
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…
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…