34 citations · 49 across the 8 of their papers we have counts for
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Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization
Henrik Schopmans, Christopher von Klitzing, Pascal Friederich
Sampling from unnormalized probability densities is a central challenge in computational science. Boltzmann generators are generative models that enable independent sampling from t…
Learning Boltzmann Generators via Constrained Mass Transport
Christopher von Klitzing, Denis Blessing, Henrik Schopmans +2
Efficient sampling from high-dimensional and multimodal unnormalized probability distributions is a central challenge in many areas of science and machine learning. We focus on Bol…
Symmetry-Aware Bayesian Flow Networks for Crystal Generation
Laura Ruple, Luca Torresi, Henrik Schopmans +1
The discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast se…
Temperature-Annealed Boltzmann Generators
Henrik Schopmans, Pascal Friederich
Efficient sampling of unnormalized probability densities such as the Boltzmann distribution of molecular systems is a longstanding challenge. Next to conventional approaches like m…
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Henrik Schopmans, Pascal Friederich
Efficient sampling of the Boltzmann distribution of molecular systems is a long-standing challenge. Recently, instead of generating long molecular dynamics simulations, generative…