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

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. Lastly, 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.
Authors:
 [1] ;  [2] ;  [3] ;  [1]
  1. Stanford Univ., Stanford, CA (United States)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
  3. SLAC National Accelerator Lab., Menlo Park, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-76SF00515
Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 8; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; catalytic mechanism; density functional theory; heterogeneous catalysis; reaction mechanisms
OSTI Identifier:
1352168

Ulissi, Zachary W., Medford, Andrew J., Bligaard, Thomas, and Nørskov, Jens K.. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. United States: N. p., Web. doi:10.1038/ncomms14621.
Ulissi, Zachary W., Medford, Andrew J., Bligaard, Thomas, & Nørskov, Jens K.. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. United States. doi:10.1038/ncomms14621.
Ulissi, Zachary W., Medford, Andrew J., Bligaard, Thomas, and Nørskov, Jens K.. 2017. "To address surface reaction network complexity using scaling relations machine learning and DFT calculations". United States. doi:10.1038/ncomms14621. https://www.osti.gov/servlets/purl/1352168.
@article{osti_1352168,
title = {To address surface reaction network complexity using scaling relations machine learning and DFT calculations},
author = {Ulissi, Zachary W. and Medford, Andrew J. and Bligaard, Thomas and Nørskov, Jens K.},
abstractNote = {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. Lastly, 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.},
doi = {10.1038/ncomms14621},
journal = {Nature Communications},
number = ,
volume = 8,
place = {United States},
year = {2017},
month = {3}
}