Machine Learning for Molecular Simulation in Quantum Chemistry
Many-Body Dynamics, Physics-Informed Models, Numerical Methods
The Research Training Group of Dr. Stefan Chmiela focuses on developing machine learning methods for molecular simulations, with a special emphasis on many-body problems in quantum chemistry. Modeling many-body problems is computationally intensive due to the rapidly growing number of non-local interactions with system size. In quantum chemistry, even the smallest practical problems already involve enough interacting electrons to render analytical solutions impossible. This combinatorial complexity carries over to the simplified atomistic picture adopted by most empirical models, where a lower number of particles interact. To address this challenge, the group develops methods that combine fundamental principles from computational physics with statistical modeling approaches to foster a better understanding of quantum phenomena in complex systems. This data-driven angle allows questions to be asked in new ways and can give rise to new perspectives on established problems.
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, Klaus-Robert Müller
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
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
BIFOLD welcomes the first six Junior Fellows
The Berlin Institute for the Foundations of Learning and Data is very pleased to announce the first six BIFOLD Junior Fellows. They were selected for the excellence of their research and are already well-established researchers in the computer sciences. In addition, their research interests show exceptional potential for BIFOLD’s research goals, either by combining machine learning and data management or by bridging the two disciplines and other research areas. The first six Junior Fellows will cover a broad range of research topics during their collaboration with BIFOLD.
New BIFOLD research groups established
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) set up two new Research Training Groups, led by Dr. Stefan Chmiela and Dr. Steffen Zeuch. The goal of these new research units at BIFOLD is to enable a junior researcher to conduct independent research and prepare him for a leadership position. Initial funding includes their own position as well as two PhD students and/or research associates for three years.
Research Group Lead
Doctoral Researcher
Doctoral Researcher
Doctoral Researcher