Dresden 2020 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 41: Computational Materials Modelling - Solids and Molecules (joint session MM/CPP)

MM 41.4: Talk

Wednesday, March 18, 2020, 16:30–16:45, IFW B

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 crystalline porous materials with many applications in chemistry and materials science, from gas separation to heterogeneous catalysis. Computer simulations of chemical processes in MOFs are severely limited by the use of classical force fields (FFs), because most FFs are unable to describe bond formation and breaking. In principle, electronic structure methods, like density-functional theory (DFT), can overcome this problem, but often the required systems are too large for routine applications of DFT. Here a high-dimensional neural network potential (NNP) is presented for a series of MOFs, which combines the advantages of both worlds - the accuracy of first principle methods with the efficiency of simple empirical potentials. We demonstrate the possibility to obtain a reliable description of the potential-energy surface of bulk MOFs based on reference calculations of molecular fragments only.