Dr. Stefan Gugler
Dr. Stefan Gugler
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.