![Dr. Stefan Chmiela Stefan Chmiela BIFOLD research group lead](https://bifold.berlin/fileadmin/_processed_/4/4/csm_Chmiela-Stefan_21.06_700x700_df075bdfbe.webp)
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
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
Huziel E. Sauceda, Luis E. Galvez-Gonzalez, Stefan Chmiela, Lauro Oliver Paz-Borbon, Klaus-Robert Müller, Alexandre Tkatchenko
BIGDML— Towards accurate quantum machine learning force fields for materials
![News](https://bifold.berlin/fileadmin/_processed_/5/d/csm_chmiela_01.23_cd0c297dce.png)
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.