Dr. Stefan Chmiela
Dr. Stefan Chmiela
Research Junior Group Lead
Dr. Stefan Chmiela leads the Research Training Group "Machine learning for molecular simulations in quantum chemistry".
| 2019 | Chorafas-Award |
- Hilbert space learning methods
- Learning from structured data
- Data efficient learning with explicit prior knowledge constraints
J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller, Oliver T. Unke
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
Adil Kabylda, J. Thorben Frank, Sergio Suarez Dou, Almaz Khabibrakhmanov, Leonardo Medrano Sandonas, Oliver T. Unke, Stefan Chmiela, Klaus-Robert Müller, Alexandre Tkatchenko
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, Gregory Cordeiro Fonseca, Ilyes Batatia , Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen, Bing Huang, Silvan Käser, Adil Kabylda, Danish Khan, Carolin Müller, Alastair J. A. Price, Kai Riedmiller, Kai Töpfer, Tsz Wai Ko, Markus Meuwly, Matthias Rupp, Gabor Csanyi, O. Anatole von Lilienfeld, Johannes T. Margraf, Klaus-Robert Müller, Alexandre Tkatchenko
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, Gregory Cordeiro Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen, Bing Huang, Silvan Käser, Adil Kabylda, Danish Khan, Carolin Müller, Alastair J. A. Price, Kai Riedmiller, Kai Töpfer, Tsz Wai Ko, Markus Meuwly, Matthias Rupp, Gabor Csanyi, O. Anatole von Lilienfeld, Johannes T. Margraf, Klaus-Robert Müller, Alexandre Tkatchenko
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller, Stefan Chmiela
A Euclidean transformer for fast and stable machine learned force fields
Simulation of quantum systems
Researchers from the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin and Google DeepMind have now developed a novel machine learning algorithm which enables highly accurate simulations of the dynamics of a single or multiple molecule on long time-scales.
Simulation of complex quantum systems
An international team of BIFOLD scientists together with scientists from the Université du Luxembourg and Google has now successfully developed a machine learning algorithm to simulate complex quantum system.
Machine Learning meets Quantum Physics
BIFOLD researchers contributed to an in-depth referenced work on the physics-based machine learning techniques that model electronic and atomistic properties of matter.