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.
Original language | English |
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Pages (from-to) | 043803 |
Journal | Physical Review Materials |
Volume | 2 |
Issue number | 4 |
DOIs | |
Publication status | Published - 17 Apr 2018 |
Fields of Expertise
- Advanced Materials Science