To address surface reaction network complexity using scaling relations machine learning and DFT calculations
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. 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:
-
- Stanford Univ., Stanford, CA (United States)
- Georgia Inst. of Technology, Atlanta, GA (United States)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1352168
- Grant/Contract Number:
- AC02-76SF00515
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 8; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; catalytic mechanism; density functional theory; heterogeneous catalysis; reaction mechanisms
Citation Formats
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., 2017.
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. https://doi.org/10.1038/ncomms14621
Ulissi, Zachary W., Medford, Andrew J., Bligaard, Thomas, and Nørskov, Jens K. Mon .
"To address surface reaction network complexity using scaling relations machine learning and DFT calculations". United States. https://doi.org/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 = {Mon Mar 06 00:00:00 EST 2017},
month = {Mon Mar 06 00:00:00 EST 2017}
}
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