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, Oliver T. Unke, Klaus-Robert Müller, Stefan Chmiela
A Euclidean transformer for fast and stable machine learned force fields
Stefan Blücher, Klaus-Robert Müller, Stefan Chmiela
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence.
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T Unke, Adil Kabylda, Huziel E Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Accurate global machine learning force fields for molecules with hundreds of atoms
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, Alexandre Tkatchenko
Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
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.