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Title: “Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors

Abstract

The rapid growth of methods emerging in the past decade for synthesis of “designer” catalysts—ranging from the size and shape-selected nanoparticles to mass-selected clusters, to precisely engineered bimetallic surfaces, to single site and pair site catalysts—has opened opportunities for tailoring the catalyst structure for the desired activity and selectivity. It has also sharpened the need for developing approaches to the operando characterization, ones that identify the catalytic active sites and follow their evolutions in reaction conditions. Commonly used methods for determination of the activity descriptors in the nanocatalysts, based on the correlation between the changes in catalyst performance and evolution of its structural and electronic properties, are hampered by the paucity of experimental techniques that can detect such properties with high accuracy and in reaction conditions. Out of many such techniques, X-ray absorption spectroscopy (XAS) stands out as an element-specific method that is very sensitive to the local geometric and electronic properties of the metal atoms and their surroundings and, therefore, is able to track catalyst structure modifications in operando conditions. Despite the vast amount of structure-specific information (such as, e.g., the charge states and radial distribution function of neighbors of selected atomic species) stored in the XAS data ofmore » catalysts, extracting it from the spectra is challenging, especially in the conditions of low metal weight loading, nanoscale dimensions, heterogeneous size and composition distributions, and harsh reaction environment. In this Perspective, we discuss the recent developments in XAS data analysis achieved by employing supervised and unsupervised machine learning (ML) methods for structural characterization of catalysts. By benefiting from the sensitivity of ML methods to subtle variations in experimental data, a previously “hidden” relationship between the X-ray absorption spectrum and descriptors of material’s structure and/or composition can be found, as illustrated on representative examples of mono-, hetero-, and nonmetallic catalysts. In the case of supervised ML, the experimental spectra can be rapidly “inverted”, and the structure of the catalyst can be tracked in real time and in reaction conditions. Finally, emerging opportunities for catalysis research that the ML methods enable, such as high-throughput data analysis, and their applications to other experimental probes of catalyst structure are discussed.« less

Authors:
 [1]; ORCiD logo [2]
+ Show Author Affiliations
  1. Max Planck Society, Berlin (Germany). Fritz-Haber-Inst., Dept. of Interface Science;
  2. Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC); Harvard Univ., Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); European Research Council (ERC)
OSTI Identifier:
1595081
Grant/Contract Number:  
SC0012573; SC0012704; FG02-03ER15476
Resource Type:
Accepted Manuscript
Journal Name:
ACS Catalysis
Additional Journal Information:
Journal Volume: 9; Journal Issue: 11; Journal ID: ISSN 2155-5435
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Timoshenko, Janis, and Frenkel, Anatoly I. “Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors. United States: N. p., 2019. Web. doi:10.1021/acscatal.9b03599.
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Timoshenko, Janis, & Frenkel, Anatoly I. “Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors. United States. https://doi.org/10.1021/acscatal.9b03599
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Timoshenko, Janis, and Frenkel, Anatoly I. Fri . "“Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors". United States. https://doi.org/10.1021/acscatal.9b03599. https://www.osti.gov/servlets/purl/1595081.
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@article{osti_1595081,
title = {“Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors},
author = {Timoshenko, Janis and Frenkel, Anatoly I.},
abstractNote = {The rapid growth of methods emerging in the past decade for synthesis of “designer” catalysts—ranging from the size and shape-selected nanoparticles to mass-selected clusters, to precisely engineered bimetallic surfaces, to single site and pair site catalysts—has opened opportunities for tailoring the catalyst structure for the desired activity and selectivity. It has also sharpened the need for developing approaches to the operando characterization, ones that identify the catalytic active sites and follow their evolutions in reaction conditions. Commonly used methods for determination of the activity descriptors in the nanocatalysts, based on the correlation between the changes in catalyst performance and evolution of its structural and electronic properties, are hampered by the paucity of experimental techniques that can detect such properties with high accuracy and in reaction conditions. Out of many such techniques, X-ray absorption spectroscopy (XAS) stands out as an element-specific method that is very sensitive to the local geometric and electronic properties of the metal atoms and their surroundings and, therefore, is able to track catalyst structure modifications in operando conditions. Despite the vast amount of structure-specific information (such as, e.g., the charge states and radial distribution function of neighbors of selected atomic species) stored in the XAS data of catalysts, extracting it from the spectra is challenging, especially in the conditions of low metal weight loading, nanoscale dimensions, heterogeneous size and composition distributions, and harsh reaction environment. In this Perspective, we discuss the recent developments in XAS data analysis achieved by employing supervised and unsupervised machine learning (ML) methods for structural characterization of catalysts. By benefiting from the sensitivity of ML methods to subtle variations in experimental data, a previously “hidden” relationship between the X-ray absorption spectrum and descriptors of material’s structure and/or composition can be found, as illustrated on representative examples of mono-, hetero-, and nonmetallic catalysts. In the case of supervised ML, the experimental spectra can be rapidly “inverted”, and the structure of the catalyst can be tracked in real time and in reaction conditions. Finally, emerging opportunities for catalysis research that the ML methods enable, such as high-throughput data analysis, and their applications to other experimental probes of catalyst structure are discussed.},
doi = {10.1021/acscatal.9b03599},
journal = {ACS Catalysis},
number = 11,
volume = 9,
place = {United States},
year = {Fri Sep 27 00:00:00 EDT 2019},
month = {Fri Sep 27 00:00:00 EDT 2019}
}
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  • Kiyohara, Shin; Tsubaki, Masashi; Liao, Kunyen
  • Journal of Physics: Materials, Vol. 2, Issue 2
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Zn K-edge XANES in nanocrystalline ZnO
journal, December 2007

Temperature-dependent EXAFS study of the local structure and lattice dynamics in cubic Y 2 O 3
journal, February 2016

  • Jonane, Inga; Lazdins, Karlis; Timoshenko, Janis
  • Journal of Synchrotron Radiation, Vol. 23, Issue 2
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The CTM4XAS program for EELS and XAS spectral shape analysis of transition metal L edges
journal, October 2010

High Throughput In Situ XAFS Screening of Catalysts
conference, January 2007

  • Tsapatsaris, Nikolaos; Beesley, Angela M.; Weiher, Norbert
  • X-RAY ABSORPTION FINE STRUCTURE - XAFS13: 13th International Conference, AIP Conference Proceedings
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Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning
journal, August 2018

  • Timoshenko, Janis; Halder, Avik; Yang, Bing
  • The Journal of Physical Chemistry C, Vol. 122, Issue 37
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SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
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The Analysis of Mixtures: Application of Principal Component Analysis to XAS Spectra
journal, April 1997

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling
journal, January 2018

Atomic configurations of Pd atoms in PdAu(111) and PdAu(100) surface alloys: Ab initio density functional calculations
journal, January 2009

X-ray physico-chemical imaging during activation of cobalt-based Fischer–Tropsch synthesis catalysts
journal, November 2017

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  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 376, Issue 2110
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A Perspective on Counting Catalytic Active Sites and Rates of Reaction Using X-Ray Spectroscopy
journal, October 2018

Extracting Knowledge from Data through Catalysis Informatics
journal, June 2018

Independent component analysis: algorithms and applications
journal, June 2000

The Use of X-ray Absorption Spectra for Validation of Classical Force-Field Models
journal, January 2016

  • Kuzmin, Alexei; Anspoks, Andris; Kalinko, Aleksandr
  • Zeitschrift für Physikalische Chemie, Vol. 230, Issue 4
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An Intelligent System for Reaction Kinetic Modeling and Catalyst Design
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Machine learning for molecular and materials science
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Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
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Is there a contraction of the interatomic distance in small metal particles?
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Operando spectroscopy: fundamental and technical aspects of spectroscopy of catalysts under working conditions
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ATHENA , ARTEMIS , HEPHAESTUS : data analysis for X-ray absorption spectroscopy using IFEFFIT
journal, June 2005

Atomic structure relaxation in nanocrystalline NiO studied by EXAFS spectroscopy: Role of nickel vacancies
journal, November 2012

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
journal, February 2017

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  • npj Computational Materials, Vol. 3, Issue 1
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Least squares quantization in PCM
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Time-resolved Studies for the Mechanism of Reduction of Copper Oxides with Carbon Monoxide:  Complex Behavior of Lattice Oxygen and the Formation of Suboxides
journal, September 2004

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  • The Journal of Physical Chemistry B, Vol. 108, Issue 36
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In Situ XAS Study on Growth of PVP-Stabilized Cu Nanoparticles
journal, July 2018

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Effects of Molecular and Electronic Structures in CoO x /CeO 2 Catalysts on NO Reduction by CO
journal, February 2019

  • Zhang, Shuhao; Li, Yuanyuan; Huang, Jiahao
  • The Journal of Physical Chemistry C, Vol. 123, Issue 12
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Extraction of Physical Parameters from X-ray Spectromicroscopy Data Using Machine Learning
journal, August 2018

Local ordering of nanostructured Pt probed by multiple-scattering XAFS
journal, September 2007

Multicomponent Signal Unmixing from Nanoheterostructures: Overcoming the Traditional Challenges of Nanoscale X-ray Analysis via Machine Learning
journal, March 2015

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures
journal, March 2019

A New Procedure for Particle Size Determination by EXAFS Based on Molecular Dynamics Simulations
journal, June 1993

Numerical Simulation of the Platinum LIII Edge White Line Relative to Nanometer Scale Clusters
journal, July 1997

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Limits and Advantages of X-ray Absorption Near Edge Structure for Nanometer Scale Metallic Clusters
journal, November 2003

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Treatment of disorder effects in X-ray absorption spectra beyond the conventional approach
journal, October 2020

A web-based library of XAFS data on model compounds
journal, May 1999

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  • Journal of Synchrotron Radiation, Vol. 6, Issue 3
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Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning
journal, December 2018

Influence of the Preparation Conditions of Oxidic NiMo/Al 2 O 3 Catalysts on the Sulfidation Ability: A Quick-XAS and Raman Spectroscopic Study
journal, October 2015

  • Rochet, Amélie; Baubet, Bertrand; Moizan, Virginie
  • The Journal of Physical Chemistry C, Vol. 119, Issue 42
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Atomic-scale identification of Pd leaching in nanoparticle catalyzed C–C coupling: effects of particle surface disorder
journal, January 2015

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Quantitative Speciation of Mn-Bearing Particulates Emitted from Autos Burning (Methylcyclopentadienyl)manganese Tricarbonyl-Added Gasolines Using XANES Spectroscopy
journal, March 2000

  • Ressler, Thorsten; Wong, Joe; Roos, Joseph
  • Environmental Science & Technology, Vol. 34, Issue 6
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Study of the local structure and oxidation state of iron in complex oxide catalysts for propylene ammoxidation
journal, January 2014

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  • Catal. Sci. Technol., Vol. 4, Issue 8
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Control of Metal Nanocrystal Size Reveals Metal-Support Interface Role for Ceria Catalysts
journal, July 2013

Wavelet analysis of extended x-ray absorption fine structure data
journal, March 2005

Self-consistent aspects of x-ray absorption calculations
journal, August 2009

Determination of bimetallic architectures in nanometer-scale catalysts by combining molecular dynamics simulations with x-ray absorption spectroscopy
journal, March 2017

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  • The Journal of Chemical Physics, Vol. 146, Issue 11
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Anomalous lattice dynamics and thermal properties of supported size- and shape-selected Pt nanoparticles
journal, October 2010

Phase speciation by extended x-ray absorption fine structure spectroscopy
journal, June 2002

  • Frenkel, Anatoly I.; Kleifeld, Oded; Wasserman, Stephen R.
  • The Journal of Chemical Physics, Vol. 116, Issue 21
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Combined Bethe-Saltpeter equations and time-dependent density-functional theory approach for x-ray absorption calculations
journal, April 2005

EXAFS study of hydrogen intercalation into ReO 3 using the evolutionary algorithm
journal, January 2014

Chemical imaging of single catalyst particles with scanning μ-XANES-CT and μ-XRF-CT
journal, January 2015

  • Price, S. W. T.; Ignatyev, K.; Geraki, K.
  • Physical Chemistry Chemical Physics, Vol. 17, Issue 1
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Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet
journal, September 2016

  • Duan, Xiaoyue; Yang, Feifei; Antono, Erin
  • Scientific Reports, Vol. 6, Issue 1
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Probing the Location and Speciation of Elements in Zeolites with Correlated Atom Probe Tomography and Scanning Transmission X‐Ray Microscopy
journal, October 2018

  • Schmidt, Joel E.; Ye, Xinwei; van Ravenhorst, Ilse K.
  • ChemCatChem, Vol. 11, Issue 1
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Real-space multiple-scattering calculation and interpretation of x-ray-absorption near-edge structure
journal, September 1998

Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory
journal, March 2017

Thermal expansion and x-ray-absorption fine-structure cumulants
journal, July 1993

Catalysis Applications of Size-Selected Cluster Deposition
journal, November 2015

Probing NiO nanocrystals by EXAFS spectroscopy
journal, December 2010

In situ formation of hydrides and carbides in palladium catalyst: When XANES is better than EXAFS and XRD
journal, April 2017

The promise of artificial intelligence in chemical engineering: Is it here, finally?
journal, December 2018

The Effect of Anharmonicity on the EXAFS Coordination Number in Small Metallic Particles
journal, January 1993

  • Clausen, Bjerne S.; Topsøe, Henrik; Hansen, Lars B.
  • Japanese Journal of Applied Physics, Vol. 32, Issue S2
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Molecular dynamics simulations of EXAFS in germanium
journal, January 2011

2D-Mapping of the Catalyst Structure Inside a Catalytic Microreactor at Work:  Partial Oxidation of Methane over Rh/Al 2 O 3
journal, May 2006

  • Grunwaldt, Jan-Dierk; Hannemann, Stefan; Schroer, Christian G.
  • The Journal of Physical Chemistry B, Vol. 110, Issue 17
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Combining theory and experiment in electrocatalysis: Insights into materials design
journal, January 2017

Determining Cu–Speciation in the Cu–CHA Zeolite Catalyst: The Potential of Multivariate Curve Resolution Analysis of In Situ XAS Data
journal, August 2018

Combining µXANES and µXRD mapping to analyse the heterogeneity in calcium carbonate granules excreted by the earthworm Lumbricus terrestris
journal, December 2013

  • Brinza, Loredana; Schofield, Paul F.; Hodson, Mark E.
  • Journal of Synchrotron Radiation, Vol. 21, Issue 1
  • DOI: 10.1107/S160057751303083X
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