Machine learning for molecular simulations, Computationally intensive modeling, Statistical modeling approaches
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. To address this challenge, the group develops methods that combine fundamental principles from computational physics with statistical modeling approaches. A data-driven angle allows questions to be asked in new ways and can give rise to new perspectives on established problems. In this context, the group will collaborate with members of BIFOLD’s Inference Systems for the Sciences and Humanities Lab (SCI-Lab).
Stefan Blücher, Klaus-Robert Müller, Stefan Chmiela
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
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
Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Research Group Lead
Doctoral Researcher