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Title: 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. Here 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. Finally, we demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Graz Univ. of Technology (Austria). Inst. of Solid State Physics
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States); Graz Univ. of Technology (Austria)
Sponsoring Org.:
USDOE Office of Science (SC); Austrian Science Fund (FWF)
OSTI Identifier:
1436084
Alternate Identifier(s):
OSTI ID: 1433460
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Physical Review Materials
Additional Journal Information:
Journal Volume: 2; Journal Issue: 4; Related Information: https://journals.aps.org/prmaterials/supplemental/10.1103/PhysRevMaterials.2.043803/PRM_SUI.pdf; Journal ID: ISSN 2475-9953
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; machine learning; DFT; interface; structure search; defect; TCNE; Ag

Citation Formats

Scherbela, Michael, Hormann, Lukas, Jeindl, Andreas, Obersteiner, Veronika, and Hofmann, Oliver T. Charting the energy landscape of metal/organic interfaces via machine learning. United States: N. p., 2018. Web. doi:10.1103/PhysRevMaterials.2.043803.
Scherbela, Michael, Hormann, Lukas, Jeindl, Andreas, Obersteiner, Veronika, & Hofmann, Oliver T. Charting the energy landscape of metal/organic interfaces via machine learning. United States. doi:10.1103/PhysRevMaterials.2.043803.
Scherbela, Michael, Hormann, Lukas, Jeindl, Andreas, Obersteiner, Veronika, and Hofmann, Oliver T. Tue . "Charting the energy landscape of metal/organic interfaces via machine learning". United States. doi:10.1103/PhysRevMaterials.2.043803.
@article{osti_1436084,
title = {Charting the energy landscape of metal/organic interfaces via machine learning},
author = {Scherbela, Michael and Hormann, Lukas and Jeindl, Andreas and Obersteiner, Veronika and Hofmann, Oliver T.},
abstractNote = {The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. Here 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. Finally, we demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.},
doi = {10.1103/PhysRevMaterials.2.043803},
journal = {Physical Review Materials},
number = 4,
volume = 2,
place = {United States},
year = {Tue Apr 17 00:00:00 EDT 2018},
month = {Tue Apr 17 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
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Works referenced in this record:

Generalized Gradient Approximation Made Simple
journal, October 1996

  • Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
  • Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
  • DOI: 10.1103/PhysRevLett.77.3865