Dresden 2020 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 30: Poster Session II

MM 30.37: Poster

Tuesday, March 17, 2020, 18:15–20:00, P4

a Neural Network Potential with electrostatic interaction — •Tsz Wai Ko and Jörg Behler — Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, German

High-dimensional neural network potentials (HDNNPs), which represent one of the most frequently used types of ML potentials, construct the short-range energy as a sum of environment-dependent atomic energy contributions. In addition, long-range electrostatic interactions can be included employing environment-dependent atomic charges. Both contributions are determined using atom-centered radial and angular symmetry functions as local structural descriptors.

Here we present benchmark calculations for several model systems such as water molecules and Zinc oxide clusters using Density Functional Theory reference calculation