activity
20242026
most citedGenerative Pseudo-Force Fields for Molecular Generation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2026

ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning

Stefan Gugler, Max Eissler, Khaled Kahouli +1

Mapping a chemical reaction network, the graph of minima and transition states (TS) and the elementary reactions connecting them, is the natural language of chemistry, from catalys…

cs.LG20261 cited

Generative Pseudo-Force Fields for Molecular Generation

Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4

Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative…

cs.LG2025

Control Variate Score Matching for Diffusion Models

Khaled Kahouli, Romuald Elie, Klaus-Robert Müller +3

Diffusion models offer a robust framework for sampling from unnormalized probability densities, which requires accurately estimating the score of the noise-perturbed target distrib…

cs.LG2025

Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models

Khaled Kahouli, Winfried Ripken, Stefan Gugler +3

The long sampling time of diffusion models remains a significant bottleneck, which can be mitigated by reducing the number of diffusion time steps. However, the quality of samples…

physics.chem-ph2024

Molecular relaxation by reverse diffusion with time step prediction

Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller +3

Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force f…