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

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.

9 pages + 2 pages of supplementary information

References in corpus (2)

• Machine learning prediction errors better than DFT accuracy
• Strongly reshaped organic-metal interfaces: Tetracyanoethylene on Cu(100)

Cited by in corpus (4)

• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• SAMPLE: Surface structure search enabled by coarse graining and statistical learning
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
• Reliable computational prediction of supramolecular ordering of complex molecules under electrochemical conditions