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

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

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)043803
JournalPhysical Review Materials
Issue number4
Publication statusPublished - 17 Apr 2018

Fields of Expertise

  • Advanced Materials Science


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