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.
Originalsprache | englisch |
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Seiten (von - bis) | 043803 |
Fachzeitschrift | Physical Review Materials |
Jahrgang | 2 |
Ausgabenummer | 4 |
DOIs | |
Publikationsstatus | Veröffentlicht - 17 Apr. 2018 |
Fields of Expertise
- Advanced Materials Science