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Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach

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Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Phys. Chem. A 2021, 125, 32, 7080-7091
    A: New Tools and Methods in Experiment and Theory

    Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach
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    • A. Martini*
      A. Martini
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      Department of Chemistry, University of Torino, Via P. Giuria 7, 10125 Torino, Italy
      *Email: [email protected]
      More by A. Martini
      Orcidhttps://orcid.org/0000-0001-8820-2157
    • A. L. Bugaev*
      A. L. Bugaev
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      Southern Scientific Centre, Russian Academy of Sciences, Chekhova 41, 344006 Rostov-on-Don, Russia
      *Email: [email protected]
      More by A. L. Bugaev
    • S. A. Guda
      S. A. Guda
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      Institute of mathematics, mechanics and computer science, Southern Federal University, Milchakova 8a, 344090 Rostov-on-Don, Russia
      More by S. A. Guda
      Orcidhttps://orcid.org/0000-0002-2398-1847
    • A. A. Guda
      A. A. Guda
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      More by A. A. Guda
      Orcidhttps://orcid.org/0000-0002-6941-4987
    • E. Priola
      E. Priola
      Department of Chemistry, University of Torino, Via P. Giuria 7, 10125 Torino, Italy
      CrisDi, Interdepartemental Center for Crystallography, University of Turin, Torino, Via P. Giuria 7, I-10125 Italy
      More by E. Priola
      Orcidhttps://orcid.org/0000-0002-0270-738X
    • E. Borfecchia
      E. Borfecchia
      Department of Chemistry, University of Torino, Via P. Giuria 7, 10125 Torino, Italy
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      Orcidhttps://orcid.org/0000-0001-8374-8329
    • S. Smolders
      S. Smolders
      Department of Microbial and Molecular Systems (M2S); Centre for Membrane separations, Adsorption, Catalysis and Spectroscopy for Sustainable Solutions (cMACS), KU Leuven, Celestijnenlaan 200F, Post box 2454, 3001 Leuven, Belgium
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    • K. Janssens
      K. Janssens
      Department of Microbial and Molecular Systems (M2S); Centre for Membrane separations, Adsorption, Catalysis and Spectroscopy for Sustainable Solutions (cMACS), KU Leuven, Celestijnenlaan 200F, Post box 2454, 3001 Leuven, Belgium
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      Orcidhttps://orcid.org/0000-0002-7194-2623
    • D. De Vos
      D. De Vos
      Department of Microbial and Molecular Systems (M2S); Centre for Membrane separations, Adsorption, Catalysis and Spectroscopy for Sustainable Solutions (cMACS), KU Leuven, Celestijnenlaan 200F, Post box 2454, 3001 Leuven, Belgium
      More by D. De Vos
      Orcidhttps://orcid.org/0000-0003-0490-9652
    • A. V. Soldatov
      A. V. Soldatov
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
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    Other Access OptionsSupporting Information (1)

    The Journal of Physical Chemistry A

    Cite this: J. Phys. Chem. A 2021, 125, 32, 7080–7091
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpca.1c03746
    Published August 5, 2021
    Copyright © 2021 American Chemical Society
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    Abstract

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    A novel approach for the analysis of extended X-ray absorption fine structure (EXAFS) spectra is developed exploiting an inverse machine learning-based algorithm. Through this approach, it is possible to explore and account for, in a precise way, the nonlinear geometry dependence of the photoelectron backscattering phases and amplitudes of single and multiple scattering paths. In addition, the determined parameters are directly related to the 3D atomic structure, without the need to use complex parametrization as in the classical fitting approach. The applicability of the approach, its potential and the advantages over the classical fit were demonstrated by fitting the EXAFS data of two molecular systems, namely, the KAu (CN)2 and the [RuCl2(CO)3]2 complexes.

    Copyright © 2021 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpca.1c03746.

    • Part S1. Fitting routine: Multidimensional coarse grid plot (Figure S1); equivalence criterion scheme (Figure S2); plot of the IHS point distributions (Figure S3); and KAu(CN)2 path datasets (Figure S4), Part S2: Experimental data, Part S3: EXAFS analysis of the KAu(CN)2 spectrum: Set of deformations employed in the fit of the KAu(CN)2 complex (Section S3.1, Figure S5); quality of prediction associated with each ML path function (KAu(CN)2 complex case) (Section 3.2, Table S1); curve-wave amplitude analysis of the main MS processes for the KAu(CN)2 case (Section S3.3, Figure S6 and Figure S7); and error curves evaluated for the variation of the α and β parameters around the minimum found by the refinement (Section S3.4 and Figure S8), Part S4. EXAFS analysis of [RuCl2(CO)3]2 complex: Set of deformations employed in the fit of the [RuCl2(CO)3]2 complex (Figure S9) and quality of prediction associated with each ML path function (the [RuCl2(CO)3]2 complex case) (Table S2), Part S5: EXAFS WT fitting routine description: EXAFS WT fit of the KAu(CN)2 complex (Section 5.1, Table S3 and Figure S10), and Part 6: Validation of the ML-refined structures through the classic EXAFS fitting procedure: KAu(CN)2 case (Section S6.1, Figure S11 and Table S4) and [RuCl2(CO)3]2 case (Section 6.2, Figure S12, and Table S5) (PDF)

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    Cited By

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    This article is cited by 10 publications.

    1. Ziyi Chen, Andrew G. Walsh, Peng Zhang. Structural Analysis of Single-Atom Catalysts by X-ray Absorption Spectroscopy. Accounts of Chemical Research 2024, 57 (4) , 521-532. https://doi.org/10.1021/acs.accounts.3c00693
    2. Alina A. Skorynina, Bogdan O. Protsenko, Oleg A. Usoltsev, Sergey A. Guda, Aram L. Bugaev. Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra. Inorganic Chemistry 2023, 62 (17) , 6608-6616. https://doi.org/10.1021/acs.inorgchem.2c04395
    3. Rachita Rana, Fernando D. Vila, Ambarish R. Kulkarni, Simon R. Bare. Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions. ACS Catalysis 2022, 12 (22) , 13813-13830. https://doi.org/10.1021/acscatal.2c03863
    4. Andrea Martini, Chiara Negri, Luca Bugarin, Gabriele Deplano, Reza K. Abasabadi, Kirill A. Lomachenko, Ton V. W. Janssens, Silvia Bordiga, Gloria Berlier, Elisa Borfecchia. Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNOx Catalysts by Machine Learning-Assisted X-ray Absorption Spectroscopy. The Journal of Physical Chemistry Letters 2022, 13 (26) , 6164-6170. https://doi.org/10.1021/acs.jpclett.2c01107
    5. Jennifer D. Lee, Jeffrey B. Miller, Anna V. Shneidman, Lixin Sun, Jason F. Weaver, Joanna Aizenberg, Juergen Biener, J. Anibal Boscoboinik, Alexandre C. Foucher, Anatoly I. Frenkel, Jessi E. S. van der Hoeven, Boris Kozinsky, Nicholas Marcella, Matthew M. Montemore, Hio Tong Ngan, Christopher R. O’Connor, Cameron J. Owen, Dario J. Stacchiola, Eric A. Stach, Robert J. Madix, Philippe Sautet, Cynthia M. Friend. Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites. Chemical Reviews 2022, 122 (9) , 8758-8808. https://doi.org/10.1021/acs.chemrev.1c00967
    6. Oleg A. Usoltsev, Aram L. Bugaev, Alexander A. Guda, Sergey A. Guda, Alexander V. Soldatov. How Much Structural Information Could Be Extracted from XANES Spectra for Palladium Hydride and Carbide Nanoparticles. The Journal of Physical Chemistry C 2022, 126 (10) , 4921-4928. https://doi.org/10.1021/acs.jpcc.1c09420
    7. Guikai Zhang, Jia Zhou, Fengfan Yang, Shengqi Chu, Hongyu Zhang, Jinfan Chang, Wenjie Xu, Tiandou Hu, Jing Zhang. An integrated and versatile QXAFS system for general XAFS beamlines. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2022, 1042 , 167428. https://doi.org/10.1016/j.nima.2022.167428
    8. K. Janssens, A. L. Bugaev, E. G. Kozyr, V. Lemmens, A. A. Guda, O. A. Usoltsev, S. Smolders, A. V. Soldatov, D. E. De Vos. Evolution of the active species of homogeneous Ru hydrodeoxygenation catalysts in ionic liquids. Chemical Science 2022, 13 (35) , 10251-10259. https://doi.org/10.1039/D2SC02150A
    9. C. D. Rankine, T. J. Penfold. Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network. The Journal of Chemical Physics 2022, 156 (16) , 164102. https://doi.org/10.1063/5.0087255
    10. Mikhail A. Soldatov, Pavel V. Medvedev, Victor Roldugin, Ivan N. Novomlinskiy, Ilia Pankin, Hui Su, Qinghua Liu, Alexander V. Soldatov. Operando Photo-Electrochemical Catalysts Synchrotron Studies. Nanomaterials 2022, 12 (5) , 839. https://doi.org/10.3390/nano12050839
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    The Journal of Physical Chemistry A

    Cite this: J. Phys. Chem. A 2021, 125, 32, 7080–7091
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpca.1c03746
    Published August 5, 2021
    Copyright © 2021 American Chemical Society
    Request reuse permissions

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