skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Abstract

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surfacemore » based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.« less

Authors:
; ; ; ; ORCiD logo;  [1]
  1. Department of Computer Science, University of North Carolina Charlotte, Charlotte, North Carolina 28223, United States
Publication Date:
Research Org.:
Univ. of South Carolina, Columbia, SC (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
OSTI Identifier:
1484052
Alternate Identifier(s):
OSTI ID: 1508777; OSTI ID: 1656917
Grant/Contract Number:  
SC0007167; AC02-05CH11231; DMREF-1534260; TG-CTS090100
Resource Type:
Published Article
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
Journal Name: Journal of Physical Chemistry. C Journal Volume: 122 Journal Issue: 49; Journal ID: ISSN 1932-7447
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Testing and assessment; Energy; Metals; Adsorption; Machine learning

Citation Formats

Chowdhury, Asif J., Yang, Wenqiang, Walker, Eric, Mamun, Osman, Heyden, Andreas, and Terejanu, Gabriel A.. Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning. United States: N. p., 2018. Web. https://doi.org/10.1021/acs.jpcc.8b09284.
Chowdhury, Asif J., Yang, Wenqiang, Walker, Eric, Mamun, Osman, Heyden, Andreas, & Terejanu, Gabriel A.. Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning. United States. https://doi.org/10.1021/acs.jpcc.8b09284
Chowdhury, Asif J., Yang, Wenqiang, Walker, Eric, Mamun, Osman, Heyden, Andreas, and Terejanu, Gabriel A.. Fri . "Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning". United States. https://doi.org/10.1021/acs.jpcc.8b09284.
@article{osti_1484052,
title = {Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning},
author = {Chowdhury, Asif J. and Yang, Wenqiang and Walker, Eric and Mamun, Osman and Heyden, Andreas and Terejanu, Gabriel A.},
abstractNote = {Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.},
doi = {10.1021/acs.jpcc.8b09284},
journal = {Journal of Physical Chemistry. C},
number = 49,
volume = 122,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1021/acs.jpcc.8b09284

Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1. Figure 1.: Reaction network for the decarboxylation and decarbonylation of propionic acid. The larger species among the metal descriptors (CHCHCO) is marked on the figure. The other descriptor (OH), along with COOH, CO2, CO, H2O, and H, is not included in the figure for clarity.

Save / Share:

Works referenced in this record:

Linear scaling relationships and volcano plots in homogeneous catalysis – revisiting the Suzuki reaction
journal, January 2015

  • Busch, Michael; Wodrich, Matthew D.; Corminboeuf, Clémence
  • Chemical Science, Vol. 6, Issue 12
  • DOI: 10.1039/C5SC02910D

Gaussian Processes for Machine Learning
journal, April 2004


Comparing Ridge and LASSO estimators for data analysis
journal, January 2017


Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012


Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015


Principal component analysis: a review and recent developments
journal, April 2016

  • Jolliffe, Ian T.; Cadima, Jorge
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 374, Issue 2065
  • DOI: 10.1098/rsta.2015.0202

Identifying Active Sites of the Water–Gas Shift Reaction over Titania Supported Platinum Catalysts under Uncertainty
journal, March 2018


Machine learning for quantum mechanics in a nutshell
journal, July 2015

  • Rupp, Matthias
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24954

Theoretical investigation of the decarboxylation and decarbonylation mechanism of propanoic acid over a Ru(0 0 0 1) model surface
journal, April 2015


Density functional theory in surface chemistry and catalysis
journal, January 2011

  • Norskov, J. K.; Abild-Pedersen, F.; Studt, F.
  • Proceedings of the National Academy of Sciences, Vol. 108, Issue 3
  • DOI: 10.1073/pnas.1006652108

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals
journal, December 2016

  • Pereira, Florbela; Xiao, Kaixia; Latino, Diogo A. R. S.
  • Journal of Chemical Information and Modeling, Vol. 57, Issue 1
  • DOI: 10.1021/acs.jcim.6b00340

Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase
journal, March 2006

  • Szaleniec, Maciej; Witko, Małgorzata; Tadeusiewicz, Ryszard
  • Journal of Computer-Aided Molecular Design, Vol. 20, Issue 3
  • DOI: 10.1007/s10822-006-9042-6

Kernel methods in machine learning
journal, June 2008

  • Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J.
  • The Annals of Statistics, Vol. 36, Issue 3
  • DOI: 10.1214/009053607000000677

Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends
journal, March 2014

  • Meredig, Bryce; Wolverton, C.
  • Chemistry of Materials, Vol. 26, Issue 6
  • DOI: 10.1021/cm403727z

Uncertainty Quantification Framework Applied to the Water–Gas Shift Reaction over Pt-Based Catalysts
journal, May 2016

  • Walker, Eric; Ammal, Salai Cheettu; Terejanu, Gabriel A.
  • The Journal of Physical Chemistry C, Vol. 120, Issue 19
  • DOI: 10.1021/acs.jpcc.6b01348

Theoretical Investigation of the Reaction Mechanism of the Decarboxylation and Decarbonylation of Propanoic Acid on Pd(111) Model Surfaces
journal, June 2012

  • Lu, Jianmin; Behtash, Sina; Heyden, Andreas
  • The Journal of Physical Chemistry C, Vol. 116, Issue 27
  • DOI: 10.1021/jp301926t

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
journal, June 2015

  • Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan
  • The Journal of Physical Chemistry Letters, Vol. 6, Issue 12
  • DOI: 10.1021/acs.jpclett.5b00831

Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces
journal, July 2007


Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007


Regularization and variable selection via the elastic net
journal, April 2005


Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
journal, July 2015

  • Rupp, Matthias; Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole
  • The Journal of Physical Chemistry Letters, Vol. 6, Issue 16
  • DOI: 10.1021/acs.jpclett.5b01456

    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.