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.LG2026

How simple can you go? An off-the-shelf transformer approach to molecular dynamics

Max Eissler, Tim Korjakow, Stefan Ganscha +3

Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other ma…

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…

cs.AI2024

Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features

Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer +5

Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abst…