3 papers
physics.comp-ph2026
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…
physics.chem-ph2026
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…
cond-mat.stat-mech2025
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…