Dr. Stefan Gugler
Dr. Stefan Gugler
Affiliation: BIFOLD
I'm a Swiss National Science Foundation funded postdoc in the group of Klaus-Robert Muller at BIFOLD at TU Berlin. Previously, I did my PhD in theoretical and computational chemistry at ETH with Markus Reiher at the intersection of machine learning with a research stay at MIT with Heather Kulik.
- Computational chemistry for catalysis
- Chemical reaction networks
- Molecular dynamics
- Generative modeling
- Swiss Chemical Society
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
Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Max Eissler, Tim Korjakow, Stefan Ganscha, Oliver T. Unke, Klaus-Robert Müller, Stefan Gugler
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
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
Dr. Stefan Gugler
Physical-Chemistry-Inspired AI for Catalyst Design
Rethinking AI architecture for molecular simulations
Classical machine learning models for molecular dynamics embed physical principles, such as energy conservation and equivariance, directly into their architectures. These inductive biases have long been seen as essential for reliable simulations. A new study challenges this assumption with a surprisingly simple approach.
Symbolic XAI
Researchers at BIFOLD have been exploring how to make AI explain itself in the same way, people explain themselves. The team’s work focuses on making AI predictions as clear and intuitive as a human explanation.