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MM: Fachverband Metall- und Materialphysik

MM 20: Poster session II

MM 20.35: Poster

Dienstag, 2. April 2019, 18:30–20:00, Poster C

Development of a Neural Network Potential for Metal-organic Frameworks — •Marius Herbold, Marco Eckhoff, and Jörg Behler — Georg-August Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany

Metal-organic frameworks (MOFs) are porous crystalline materials with many applications in chemistry and materials science, from gas separation to heterogeneous catalysis. To date, computer simulations of chemical processes in MOFs are severely limited by the use of classical force fields, which in most cases are unable to describe the making and breaking of bonds. Electronic structure methods like density-functional theory (DFT) in principle offer a solution for this problem, but often the required systems are too large for routine applications of DFT. Here we present a high-dimensional neural network potential for a series of MOFs, which combines the accuracy of first principles with the efficiency of simple empirical potentials. We demonstrate that it is possible to obtain a reliable description of the potential-energy surface based on reference calculations of molecular fragments only.