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

Autor:	Scherbela, Michael, Hörmann, Lukas, Jeindl, Andreas, Obersteiner, Veronika, Hofmann, Oliver T.
Rok vydání:	2017
Předmět:	Condensed Matter - Materials Science
Zdroj:	Phys. Rev. Materials 2, 043803 (2018)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevMaterials.2.043803
Popis:	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. Comment: 9 pages + 2 pages of supplementary information
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1709.05417 Zobrazit plný text záznamu View this record from Arxiv