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DOI: 10.5445/IR/1000082984
Veröffentlicht am 22.05.2018
DOI: 10.1038/s41598-018-21070-0
Zitationen: 1
Web of Science
Zitationen: 1

Machine learning of correlated dihedral potentials for atomistic molecular force fields

Friederich, Pascal; Konrad, Manuel; Strunk, Timo; Wenzel, Wolfgang

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. We demonstrate with the example of α-NPD, a molecule frequently used in organic electronics, that this approach outperforms traditional force fields by decreasing the mean absolute deviations by one order of magnitude to values smaller than 0.37 kcal/mol (16.0 meV) per dihedral angle.

Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2045-2322
URN: urn:nbn:de:swb:90-829849
KITopen ID: 1000082984
HGF-Programm 43.21.04; LK 01
Erschienen in Scientific reports
Band 8
Heft 1
Seiten Article: 2559
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte Atomistic models; Computational methods; Electronic devices; Electronic materials
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