Open Access

Nature Communications, volume 8, issue 1, publication number 14621

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Zachary W Ulissi ¹

Andrew J. Medford ²

Thomas Bligaard ³

Jens K. Nørskov ¹

Hide authors affiliations Show authors affiliations: 3 affiliations

Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, Stanford, USA |

School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, USA |

SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, USA |

Publication type: Journal Article

Publication date: 2017-03-06

Springer Nature

Journal: Nature Communications

Quartile SCImago

Quartile WOS

Impact factor: 16.6

ISSN: 20411723, 20411723

DOI: 10.1038/ncomms14621

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General Chemistry

General Biochemistry, Genetics and Molecular Biology

General Physics and Astronomy

Abstract

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations. Finding catalyst mechanisms remains a challenge due to the complexity of hydrocarbon chemistry. Here, the authors shows that scaling relations and machine-learning methods can focus full-accuracy methods on the small subset of rate-limiting reactions allowing larger reaction networks to be treated.

By date By citations

Top-30

Citations by journals

	5 10 15 20 25 30 35 40
ACS Catalysis	ACS Catalysis, 39, 9.22% ACS Catalysis 39 publications, 9.22%
Journal of Physical Chemistry C	Journal of Physical Chemistry C, 18, 4.26% Journal of Physical Chemistry C 18 publications, 4.26%
Physical Chemistry Chemical Physics	Physical Chemistry Chemical Physics, 16, 3.78% Physical Chemistry Chemical Physics 16 publications, 3.78%
Journal of Physical Chemistry Letters	Journal of Physical Chemistry Letters, 13, 3.07% Journal of Physical Chemistry Letters 13 publications, 3.07%
Journal of Chemical Physics	Journal of Chemical Physics, 12, 2.84% Journal of Chemical Physics 12 publications, 2.84%
ChemCatChem	ChemCatChem, 12, 2.84% ChemCatChem 12 publications, 2.84%
Chemical Reviews	Chemical Reviews, 11, 2.6% Chemical Reviews 11 publications, 2.6%
Nature Communications	Nature Communications, 10, 2.36% Nature Communications 10 publications, 2.36%
Chemical Science	Chemical Science, 10, 2.36% Chemical Science 10 publications, 2.36%
Catalysis Science and Technology	Catalysis Science and Technology, 9, 2.13% Catalysis Science and Technology 9 publications, 2.13%
Journal of Chemical Information and Modeling	Journal of Chemical Information and Modeling, 8, 1.89% Journal of Chemical Information and Modeling 8 publications, 1.89%
Nature Catalysis	Nature Catalysis, 8, 1.89% Nature Catalysis 8 publications, 1.89%
npj Computational Materials	npj Computational Materials, 8, 1.89% npj Computational Materials 8 publications, 1.89%
Journal of Chemical Theory and Computation	Journal of Chemical Theory and Computation, 8, 1.89% Journal of Chemical Theory and Computation 8 publications, 1.89%
Journal of Physical Chemistry A	Journal of Physical Chemistry A, 8, 1.89% Journal of Physical Chemistry A 8 publications, 1.89%
Advanced Materials	Advanced Materials, 6, 1.42% Advanced Materials 6 publications, 1.42%
Industrial & Engineering Chemistry Research	Industrial & Engineering Chemistry Research, 6, 1.42% Industrial & Engineering Chemistry Research 6 publications, 1.42%
Applied Surface Science	Applied Surface Science, 5, 1.18% Applied Surface Science 5 publications, 1.18%
Current Opinion in Chemical Engineering	Current Opinion in Chemical Engineering, 5, 1.18% Current Opinion in Chemical Engineering 5 publications, 1.18%
Journal of Materials Chemistry A	Journal of Materials Chemistry A, 5, 1.18% Journal of Materials Chemistry A 5 publications, 1.18%
Wiley Interdisciplinary Reviews: Computational Molecular Science	Wiley Interdisciplinary Reviews: Computational Molecular Science, 4, 0.95% Wiley Interdisciplinary Reviews: Computational Molecular Science 4 publications, 0.95%
Physical Review Materials	Physical Review Materials, 4, 0.95% Physical Review Materials 4 publications, 0.95%
Applied Catalysis B: Environmental	Applied Catalysis B: Environmental, 4, 0.95% Applied Catalysis B: Environmental 4 publications, 0.95%
Chemical Engineering Journal	Chemical Engineering Journal, 4, 0.95% Chemical Engineering Journal 4 publications, 0.95%
Molecular Catalysis	Molecular Catalysis, 4, 0.95% Molecular Catalysis 4 publications, 0.95%
Chemical Society Reviews	Chemical Society Reviews, 4, 0.95% Chemical Society Reviews 4 publications, 0.95%
Topics in Catalysis	Topics in Catalysis, 3, 0.71% Topics in Catalysis 3 publications, 0.71%
Chem Catalysis	Chem Catalysis, 3, 0.71% Chem Catalysis 3 publications, 0.71%
Coordination Chemistry Reviews	Coordination Chemistry Reviews, 3, 0.71% Coordination Chemistry Reviews 3 publications, 0.71%
	5 10 15 20 25 30 35 40

Citations by publishers

	20 40 60 80 100 120 140
American Chemical Society (ACS)	American Chemical Society (ACS), 127, 30.02% American Chemical Society (ACS) 127 publications, 30.02%
Elsevier	Elsevier, 84, 19.86% Elsevier 84 publications, 19.86%
Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 60, 14.18% Royal Society of Chemistry (RSC) 60 publications, 14.18%
Wiley	Wiley, 49, 11.58% Wiley 49 publications, 11.58%
Springer Nature	Springer Nature, 47, 11.11% Springer Nature 47 publications, 11.11%
American Institute of Physics (AIP)	American Institute of Physics (AIP), 14, 3.31% American Institute of Physics (AIP) 14 publications, 3.31%
IOP Publishing	IOP Publishing, 6, 1.42% IOP Publishing 6 publications, 1.42%
American Physical Society (APS)	American Physical Society (APS), 5, 1.18% American Physical Society (APS) 5 publications, 1.18%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 4, 0.95% Multidisciplinary Digital Publishing Institute (MDPI) 4 publications, 0.95%
The Chemical Society of Japan	The Chemical Society of Japan, 3, 0.71% The Chemical Society of Japan 3 publications, 0.71%
Annual Reviews	Annual Reviews, 3, 0.71% Annual Reviews 3 publications, 0.71%
Institution of Chemical Engineers	Institution of Chemical Engineers, 2, 0.47% Institution of Chemical Engineers 2 publications, 0.47%
Cambridge University Press	Cambridge University Press, 2, 0.47% Cambridge University Press 2 publications, 0.47%
Proceedings of the National Academy of Sciences (PNAS)	Proceedings of the National Academy of Sciences (PNAS), 2, 0.47% Proceedings of the National Academy of Sciences (PNAS) 2 publications, 0.47%
Taylor & Francis	Taylor & Francis, 2, 0.47% Taylor & Francis 2 publications, 0.47%
American Vacuum Society	American Vacuum Society, 1, 0.24% American Vacuum Society 1 publication, 0.24%
EDP Sciences	EDP Sciences, 1, 0.24% EDP Sciences 1 publication, 0.24%
IOS Press	IOS Press, 1, 0.24% IOS Press 1 publication, 0.24%
American Association for the Advancement of Science (AAAS)	American Association for the Advancement of Science (AAAS), 1, 0.24% American Association for the Advancement of Science (AAAS) 1 publication, 0.24%
Chinese Academy of Sciences	Chinese Academy of Sciences, 1, 0.24% Chinese Academy of Sciences 1 publication, 0.24%
IEEE	IEEE, 1, 0.24% IEEE 1 publication, 0.24%
Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii	Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii, 1, 0.24% Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii 1 publication, 0.24%
	20 40 60 80 100 120 140

We do not take into account publications without a DOI.
Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
Statistics recalculated weekly.

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GOST Copy

Ulissi Z. W. et al. To address surface reaction network complexity using scaling relations machine learning and DFT calculations // Nature Communications. 2017. Vol. 8. No. 1. 14621

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Ulissi Z. W., Medford A. J., Bligaard T., Nørskov J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations // Nature Communications. 2017. Vol. 8. No. 1. 14621

RIS |

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RIS Copy

TY - JOUR

DO - 10.1038/ncomms14621

UR - https://doi.org/10.1038/ncomms14621

TI - To address surface reaction network complexity using scaling relations machine learning and DFT calculations

T2 - Nature Communications

AU - Ulissi, Zachary W

AU - Medford, Andrew J.

AU - Bligaard, Thomas

AU - Nørskov, Jens K.

PY - 2017

DA - 2017/03/06 00:00:00

PB - Springer Nature

IS - 1

VL - 8

SN - 2041-1723

ER -

BibTex

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BibTex Copy

@article{2017_Ulissi,

author = {Zachary W Ulissi and Andrew J. Medford and Thomas Bligaard and Jens K. Nørskov},

title = {To address surface reaction network complexity using scaling relations machine learning and DFT calculations},

journal = {Nature Communications},

year = {2017},

volume = {8},

publisher = {Springer Nature},

month = {mar},

url = {https://doi.org/10.1038/ncomms14621},

number = {1},

doi = {10.1038/ncomms14621}

}

Found error?

Publisher

Springer Nature

Journal

Nature Communications

Quartile SCImago

Quartile WOS

Impact factor

16.6

ISSN

20411723 (Print)
20411723 (Electronic)