Berlin 2018 – scientific programme
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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 20: Poster Session I
CPP 20.25: Poster
Monday, March 12, 2018, 17:30–19:30, Poster A
Machine learning of correlated dihedral potentials for atomistic molecular force fields — •Pascal Friederich1, Manuel Konrad1, Timo Strunk2, and Wolfgang Wenzel1 — 1Karlsruhe Institute of Technology, Karlrsuhe, Deutschland — 2Nanomatch GmbH, Karlrsuhe, Deutschland
Computer simulation increasingly complements experimental efforts to describe nanoscale structure formation. Molecular mechanics simulations and related computational methods fundamentally rely on the accuracy of classical atomistic force fields for the evaluation of inter- and intramolecular energies. One indispensable component of such force fields, in particular for large organic molecules, is the accuracy of molecule-specific dihedral potentials which are the key determinants of molecular flexibility. We show in this work that non-local correlations of dihedral potentials play a decisive role in the description of the total molecular energy - an effect which is neglected in most state-of-the-art dihedral force fields. We furthermore present an efficient machine learning approach to compute intramolecular conformational energies. At the example of α-NPD, a prototypical molecule used in organic electronics, we demonstrate that this approach improves the agreement between semi-empirical energies and traditional force fields by one order of magnitude to a mean absolute deviation smaller than 0.37 kcal/mol (16.0 meV) per dihedral angle.