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Usoltsev O, Tereshchenko A, Skorynina A, Kozyr E, Soldatov A, Safonova O, Clark AH, Ferri D, Nachtegaal M, Bugaev A. Machine Learning for Quantitative Structural Information from Infrared Spectra: The Case of Palladium Hydride. SMALL METHODS 2024; 8:e2301397. [PMID: 38295064 DOI: 10.1002/smtd.202301397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/09/2024] [Indexed: 02/02/2024]
Abstract
Infrared spectroscopy (IR) is a widely used technique enabling to identify specific functional groups in the molecule of interest based on their characteristic vibrational modes or the presence of a specific adsorption site based on the characteristic vibrational mode of an adsorbed probe molecule. The interpretation of an IR spectrum is generally carried out within a fingerprint paradigm by comparing the observed spectral features with the features of known references or theoretical calculations. This work demonstrates a method for extracting quantitative structural information beyond this approach by application of machine learning (ML) algorithms. Taking palladium hydride formation as an example, Pd-H pressure-composition isotherms are reconstructed using IR data collected in situ in diffuse reflectance using CO molecule as a probe. To the best of the knowledge, this is the first example of the determination of continuous structural descriptors (such as interatomic distance and stoichiometric coefficient) from the fine structure of vibrational spectra, which opens new possibilities of using IR spectra for structural analysis.
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Affiliation(s)
- Oleg Usoltsev
- ALBA Synchrotron, Cerdanyola del Valles, Barcelona, 08290, Spain
| | | | - Alina Skorynina
- ALBA Synchrotron, Cerdanyola del Valles, Barcelona, 08290, Spain
| | | | - Alexander Soldatov
- Southern Federal University, Sladkova 178/24, Rostov-on-Don, 344090, Russia
| | - Olga Safonova
- Paul Scherrer Institute, Forschungsstrasse 111, Villigen, 5232, Switzerland
| | - Adam H Clark
- Paul Scherrer Institute, Forschungsstrasse 111, Villigen, 5232, Switzerland
| | - Davide Ferri
- Paul Scherrer Institute, Forschungsstrasse 111, Villigen, 5232, Switzerland
| | - Maarten Nachtegaal
- Paul Scherrer Institute, Forschungsstrasse 111, Villigen, 5232, Switzerland
| | - Aram Bugaev
- Paul Scherrer Institute, Forschungsstrasse 111, Villigen, 5232, Switzerland
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Abrosimov SV, Protsenko BO, Mannaa AS, Vlasenko VG, Guda SA, Pankin IA, Burlov AS, Koshchienko YV, Guda AA, Soldatov AV. Improving sensitivity of XANES structural fit to the bridged metal-metal coordination. JOURNAL OF SYNCHROTRON RADIATION 2024; 31:447-455. [PMID: 38530834 DOI: 10.1107/s1600577524002091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 03/03/2024] [Indexed: 03/28/2024]
Abstract
Hard X-ray absorption spectroscopy is a valuable in situ probe for non-destructive diagnostics of metal sites. The low-energy interval of a spectrum (XANES) contains information about the metal oxidation state, ligand type, symmetry and distances in the first coordination shell but shows almost no dependency on the bridged metal-metal bond length. The higher-energy interval (EXAFS), on the contrary, is more sensitive to the coordination numbers and can decouple the contribution from distances in different coordination shells. Supervised machine-learning methods can combine information from different intervals of a spectrum; however, computational approaches for the near-edge region of the spectrum and higher energies are different. This work aims to keep all benefits of XANES and extend its sensitivity towards the interatomic distances in the first and second coordination shells. Using a binuclear bridged copper complex as a case study and cross-validation analysis as a quantitative tool it is shown that the first 170 eV above the edge are already sufficient to balance the contributions of Cu-O/N scattering and Cu-Cu scattering. As a more general outcome this work highlights the trivial but often overlooked importance of using `longer' energy intervals of XANES for structural refinement and machine-learning predictions. The first 200 eV above the absorption edge still do not require parametrization of Debye-Waller damping and can be calculated within full multiple scattering or finite difference approximations with only moderately increased computational costs.
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Affiliation(s)
- S V Abrosimov
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - B O Protsenko
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - A S Mannaa
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - V G Vlasenko
- Institute of Physics, Southern Federal University, Stachki Ave 194, 344090 Rostov-on-Don, Russian Federation
| | - S A Guda
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - I A Pankin
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - A S Burlov
- Institute of Physical and Organic Chemistry, Stachki Ave 194/2, 344090 Rostov-on-Don, Russian Federation
| | - Y V Koshchienko
- Institute of Physical and Organic Chemistry, Stachki Ave 194/2, 344090 Rostov-on-Don, Russian Federation
| | - A A Guda
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
| | - A V Soldatov
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russian Federation
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Timoshenko J, Haase FT, Saddeler S, Rüscher M, Jeon HS, Herzog A, Hejral U, Bergmann A, Schulz S, Roldan Cuenya B. Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning. J Am Chem Soc 2023; 145:4065-4080. [PMID: 36762901 PMCID: PMC9951215 DOI: 10.1021/jacs.2c11824] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Indexed: 02/11/2023]
Abstract
Bimetallic transition-metal oxides, such as spinel-like CoxFe3-xO4 materials, are known as attractive catalysts for the oxygen evolution reaction (OER) in alkaline electrolytes. Nonetheless, unveiling the real active species and active states in these catalysts remains a challenge. The coexistence of metal ions in different chemical states and in different chemical environments, including disordered X-ray amorphous phases that all evolve under reaction conditions, hinders the application of common operando techniques. Here, we address this issue by relying on operando quick X-ray absorption fine structure spectroscopy, coupled with unsupervised and supervised machine learning methods. We use principal component analysis to understand the subtle changes in the X-ray absorption near-edge structure spectra and develop an artificial neural network to decipher the extended X-ray absorption fine structure spectra. This allows us to separately track the evolution of tetrahedrally and octahedrally coordinated species and to disentangle the chemical changes and several phase transitions taking place in CoxFe3-xO4 catalysts and on their active surface, related to the conversion of disordered oxides into spinel-like structures, transformation of spinels into active oxyhydroxides, and changes in the degree of spinel inversion in the course of the activation treatment and under OER conditions. By correlating the revealed structural changes with the distinct catalytic activity for a series of CoxFe3-xO4 samples, we elucidate the active species and OER mechanism.
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Affiliation(s)
- Janis Timoshenko
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Felix T. Haase
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Sascha Saddeler
- Institute
of Inorganic Chemistry and Center for Nanointegration Duisburg-Essen
(CENIDE), University of Duisburg-Essen, 45117 Essen, Germany
| | - Martina Rüscher
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Hyo Sang Jeon
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Antonia Herzog
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Uta Hejral
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Arno Bergmann
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
| | - Stephan Schulz
- Institute
of Inorganic Chemistry and Center for Nanointegration Duisburg-Essen
(CENIDE), University of Duisburg-Essen, 45117 Essen, Germany
| | - Beatriz Roldan Cuenya
- Department
of Interface Science, Fritz-Haber Institute
of the Max-Planck Society, 14195 Berlin, Germany
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Rankine CD, Penfold TJ. Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network. J Chem Phys 2022; 156:164102. [PMID: 35490005 DOI: 10.1063/5.0087255] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The affordable, accurate, and generalizable prediction of spectroscopic observables plays a key role in the analysis of increasingly complex experiments. In this article, we develop and deploy a deep neural network-XANESNET-for predicting the lineshape of first-row transition metal K-edge x-ray absorption near-edge structure (XANES) spectra. XANESNET predicts the spectral intensities using only information about the local coordination geometry of the transition metal complexes encoded in a feature vector of weighted atom-centered symmetry functions. We address in detail the calibration of the feature vector for the particularities of the problem at hand, and we explore the individual feature importance to reveal the physical insight that XANESNET obtains at the Fe K-edge. XANESNET relies on only a few judiciously selected features-radial information on the first and second coordination shells suffices along with angular information sufficient to separate satisfactorily key coordination geometries. The feature importance is found to reflect the XANES spectral window under consideration and is consistent with the expected underlying physics. We subsequently apply XANESNET at nine first-row transition metal (Ti-Zn) K-edges. It can be optimized in as little as a minute, predicts instantaneously, and provides K-edge XANES spectra with an average accuracy of ∼±2%-4% in which the positions of prominent peaks are matched with a >90% hit rate to sub-eV (∼0.8 eV) error.
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Affiliation(s)
- C D Rankine
- Chemistry-School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, United Kingdom
| | - T J Penfold
- Chemistry-School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, United Kingdom
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