Charting the energy landscape of metal/organic interfaces via machine learning

Michael Scherbela, Lukas Hörmann, Andreas Jeindl, Veronika Obersteiner, Oliver Hofmann

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

Abstract

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.
Originalspracheenglisch
Seiten (von - bis)043803
FachzeitschriftPhysical Review Materials
Jahrgang2
Ausgabenummer4
DOIs
PublikationsstatusVeröffentlicht - 17 Apr. 2018

Fields of Expertise

  • Advanced Materials Science

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