Regensburg 2022 – scientific programme

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SYES: Frontiers of Electronic-Structure Theory: Focus on Artificial Intelligence Applied to Real Materials

SYES 1: Frontiers of Electronic-Structure Theory: Focus on Artificial Intelligence Applied to Real Materials

SYES 1.3: Invited Talk

Thursday, September 8, 2022, 16:00–16:30, H1

Four Generations of Neural Network Potentials — •Jörg Behler — Universität Göttingen, Germany

A lot of progress has been made in recent years in the development of machine learning potentials for atomistic simulations, with neural network potentials (NNPs) being an important example. While the first generation of NNPs has been restricted to small systems, the second generation extended the applicability of ML potentials 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 NNPs employing environment-dependent charges, but only recently limitations of this locality approximation could be overcome by the introduction of fourth-generation NNPs, which are able to describe non-local charge transfer using a global charge equilibration step. In this talk an overview about the historical evolution of high-dimensional neural network potentials will be given along with an overview of typical applications in large-scale atomistic simulations.