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Machine Learning for Molecular Simulation in Quantum Chemistry


Dr. Stefan Chmiela


Technische Universität Berlin
Marchstraße 23, 10587 Berlin

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).

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

June 29 , 2022

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

December 14 , 2021

Dr. Stefan Chmiela

Research Group Lead

Thorben Frank Bifold Researcher

Thorben Frank

Doctoral Researcher