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Catalysis. Assessing the reliability of calculated catalytic ammonia synthesis rates.

Authors
  • Medford, Andrew J1
  • Wellendorff, Jess1
  • Vojvodic, Aleksandra2
  • Studt, Felix2
  • Abild-Pedersen, Frank2
  • Jacobsen, Karsten W3
  • Bligaard, Thomas2
  • Nørskov, Jens K4
  • 1 SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA. SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
  • 2 SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.
  • 3 Center for Atomic-scale Materials Design (CAMD), Department of Physics, Technical University of Denmark, DK-2800 Lyngby, Denmark. , (Denmark)
  • 4 SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA. SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA. [email protected]
Type
Published Article
Journal
Science
Publisher
American Association for the Advancement of Science (AAAS)
Publication Date
Jul 11, 2014
Volume
345
Issue
6193
Pages
197–200
Identifiers
DOI: 10.1126/science.1253486
PMID: 25013071
Source
Medline
License
Unknown

Abstract

We introduce a general method for estimating the uncertainty in calculated materials properties based on density functional theory calculations. We illustrate the approach for a calculation of the catalytic rate of ammonia synthesis over a range of transition-metal catalysts. The correlation between errors in density functional theory calculations is shown to play an important role in reducing the predicted error on calculated rates. Uncertainties depend strongly on reaction conditions and catalyst material, and the relative rates between different catalysts are considerably better described than the absolute rates. We introduce an approach for incorporating uncertainty when searching for improved catalysts by evaluating the probability that a given catalyst is better than a known standard.

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