7 citations · 9 across the 4 of their papers we have counts for
4 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…
Amortized Sampling with Transferable Normalizing Flows
Charlie B. Tan, Majdi Hassan, Leon Klein +5
Efficient equilibrium sampling of molecular conformations remains a core challenge in computational chemistry and statistical inference. Classical approaches such as molecular dyna…
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Charlie B. Tan, Avishek Joey Bose, Chen Lin +3
Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators tackle this problem by pairing normaliz…
Transferable Boltzmann Generators
Leon Klein, Frank Noé
The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method th…