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Khaled Kahouli

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Technische Universität Berlin
Machine Learning for Molecular Simulation in Quantum Chemistry

Marchstraße 23, 10587 Berlin
https://web.ml.tu-berlin.de/

© BIFOLD

Khaled Kahouli

Doctoral Researcher

Khaled Kahouli is a PhD student in the Machine Learning Group at TU Berlin and a member of the BIFOLD graduate school. He completed his M.Sc in Computer Science at TU Berlin in 2023, specializing in generative modeling in quantum chemistry. Since 2023, his research has been centered on generative models, specifically focusing on molecule generation and structure optimization.

  • Generative Models
  • Machine Learning for Quantum Chemistry
  • Graph Neural Networks

Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler, Michael Plainer, Frank Noé, Klaus-Robert Müller, Niklas Wolf Andreas Gebauer

Generative Pseudo-Force Fields for Molecular Generation

May 18, 2026
https://doi.org/10.48550/arXiv.2605.19050

Khaled Kahouli, Romuald Elie, Klaus-Robert Müller, Quentin Berthet, Oliver T. Unke, Arnaud Doucet

Control Variate Score Matching for Diffusion Models

December 23, 2025
https://doi.org/10.48550/arXiv.2512.20003

Khaled Kahouli, Winfried Ripken, Stefan Gugler, Oliver T. Unke, Klaus-Robert Müller, Shinichi Nakajima

ENHANCING DIFFUSION MODELS EFFICIENCY BY DISENTANGLING TOTAL-VARIANCE AND SIGNAL-TO-NOISE RATIO

February 12, 2025
https://arxiv.org/pdf/2502.08598

Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler, Niklas Wolf Andreas Gebauer

Molecular relaxation by reverse diffusion with time step prediction

August 06, 2024
https://doi.org/10.1088/2632-2153/ad652c