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Stefaan Hessmann

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Technische Universität Berlin
Machine Learning

Marchstraße 23, 10587 Berlin
https://www.tu.berlin/en/ml

Stefaan Hessmann BIFOLD researcher
© Hessmann

Stefaan Hessmann

Doctoral Researcher

Stefaan Hessmann is a doctoral researcher at the Machine Leraning group at Technische Universität Berlin. He obtained his Bachelor's degree in Physics from Technische Universität Dresden and subsequently pursued his studies in Computational Sciences at Freie Universität Berlin. At present, his research primarily focuses on the application of deep neural networks in the field of materials sciences. 
 

  • Quantum Chemistry
  • Deep Learning
  • Materials Science
  • Generative Models
     

Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer, Michael Gastegger, Tamio Oguchi, Tomoki Yamashita

Accelerating crystal structure search through active learning with neural networks for rapid relaxations

August 07, 2024
https://arxiv.org/pdf/2408.04073

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

Kristof T. Schütt, Stefaan S.P. Hessmann, Niklas W.A. Gebauer, Jonas Lederer, Michael Gastegger

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

April 12, 2023
https://doi.org/10.1063/5.0138367

News
BIFOLD Update| Feb 21, 2025

Guided exploration of chemical structures

The development of new, stable materials has enabled significant advancements across various research fields. A key challenge in this process is global energy optimization. The Agility Project "Guided Exploration of Chemical Space with Deep Neural Networks and Bayesian Optimization" has contributed to the advancement of current research in this area.