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arXiv · 1709.05417

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

Michael Scherbela·Lukas Hörmann·Andreas Jeindl·Veronika Obersteiner·Oliver T. Hofmann
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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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Michael Scherbela, Lukas Hörmann, Andreas Jeindl, Veronika Obersteiner, Oliver T. Hofmann. 2017-09-15. Leaving the Valley: Charting the Energy Landscape of Metal/Organic Interfaces via Machine Learning. https://doi.org/10.1103/physrevmaterials.2.043803

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