Atomistic Simulations with High-Dimensional Neural Network Potentials

Atomistic Simulations with High-Dimensional Neural Network Potentials

In recent years, a lot of progress has been made in the development of machine learning potentials (MLPs) turning them into a standard tool for atomistic simulations in chemistry and materials science. 

While the first generation of MLPs has been restricted to small molecules with only a few degrees of freedom, the second generation extended the applicability of MLPs to high-dimensional systems containing thousands of atoms by constructing the total energy as a sum of environment-dependent atomic energies. Long-range electrostatic interactions can be included in third-generation MLPs employing environment-dependent charges. Only recently limitations related to the underlying locality approximation could be overcome by increasingly popular fourth-generation potentials, which are able to describe non-local charge transfer and multiple charge states. 

In this talk, the evolution of MLPs will be illustrated using high-dimensional neural networks, an important and frequently used type of MLPs. Some examples concerning their applicability will be presented.

Jörg Behler
Full Professor of Theoretical Chemistry

In 2017, Jörg Behler joined the University of Göttingen as a Full Professor of Theoretical Chemistry.