Leaving the Valley: Charting the Energy Landscape of Metal/Organic
Interfaces via Machine Learning
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by
Michael Scherbela, Lukas Hörmann, Andreas Jeindl, Veronika
Obersteiner, Oliver T. Hofmann
2017
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.
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