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Machine Learning for Molecular Simulation in Quantum Chemistry


Dr. Stefan Chmiela


Technische Universität Berlin
Marchstraße 23, 10587 Berlin

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

May 05 , 2017

Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko

Towards exact molecular dynamics simulations with machine-learned force fields

September 24 , 2018

Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

July 02 , 2019

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

January 11 , 2023

LTR: Dr. Kaustubh Beedkar, Dr. Jan Hermann, Dr. Marina Marie-Claire Höhne, Dr. Danh Le Phuoc, Dr. Kristof Schütt, Dr. Eleni Tzirita Zacharatou
July 22, 2021

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.

May 12, 2021

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.

Stefan Chmiela BIFOLD research group lead

Dr. Stefan Chmiela

Research Group Lead

Stefan Blücher

Doctoral Researcher

Mihail Bogojeski

Doctoral Researcher

Thorben Frank Bifold Researcher

Thorben Frank

Doctoral Researcher