Leaving the Valley: Charting the Energy Landscape of Metal/Organic Interfaces via Machine Learning release_lscuntfutbctlmyl7v4xqlz3se

by Michael Scherbela, Lukas Hörmann, Andreas Jeindl, Veronika Obersteiner, Oliver T. Hofmann

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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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Date   2017-12-13
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arXiv  1709.05417v2
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