The paper “Machine Learning for Molecular Simulation” by BIFOLD Co-Director Prof. Dr. Klaus-Robert Müller, Principal Investigator Prof. Dr. Frank Noé and colleagues was among the top 10 most downloaded physical science articles in the Annual Reviews in 2020.
Machine Learning has a growing influence in the physical sciences. In 2020 BIFOLD researchers contributed to major scientific advances, especially in the field of Machine Learning for quantum chemistry. As a result of cooperations on a national and international level BIFOLD researchers achieved i.e. a scientific breakthrough by proposing a reinforced learning method to separate and move single molecules out of a structure, developed a Deep Learning method to solve Schroedingers equation more accurately and leveraged Machine Learning to achieve high quantum chemical accuracy from density functional approximations.
The paper “Machine Learning for Molecular Simulation” by Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller and Cecilia Clementi is another example of a high impact publication in quantum mechanics. The authors review Machine Learning methods for molecular simulation with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics.
The paper is among the most downloaded Annual Reviews articles of 2020, specifically one of the ten most downloaded physical science articles. Both Prof. Dr. Klaus-Robert Müller and Prof. Dr. Frank Noé were recently also featured in the Clarivate™ 2020 Highly Cited Researchers™ list, emphasizing their leading role in the international research community in the interdisciplinary area of computer science and chemistry.
THE PAPER IN DETAIL:
Authors:
Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Abstract:
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation
Publication:
Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi: Machine Learning for Molecular Simulation.Annual Review of Physical Chemistry, Vol. 71: 361-390
https://doi.org/10.1146/annurev-physchem-042018-052331
Authors:
Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Abstract:
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation
Publication:
Frank Noé Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi: Machine Learning for Molecular Simulation.Annual Review of Physical Chemistry, Vol. 71: 361-390
https://doi.org/10.1146/annurev-physchem-042018-052331