1
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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: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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2
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Wang J, Hsu CS, Wu TS, Chan TS, Suen NT, Lee JF, Chen HM. In situ X-ray spectroscopies beyond conventional X-ray absorption spectroscopy on deciphering dynamic configuration of electrocatalysts. Nat Commun 2023; 14:6576. [PMID: 37852958 PMCID: PMC10584842 DOI: 10.1038/s41467-023-42370-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 10/04/2023] [Indexed: 10/20/2023] Open
Abstract
Realizing viable electrocatalytic processes for energy conversion/storage strongly relies on an atomic-level understanding of dynamic configurations on catalyst-electrolyte interface. X-ray absorption spectroscopy (XAS) has become an indispensable tool to in situ investigate dynamic natures of electrocatalysts but still suffers from limited energy resolution, leading to significant electronic transitions poorly resolved. Herein, we highlight advanced X-ray spectroscopies beyond conventional XAS, with emphasis on their unprecedented capabilities of deciphering key configurations of electrocatalysts. The profound complementarities of X-ray spectroscopies from various aspects are established in a probing energy-dependent "in situ spectroscopy map" for comprehensively understanding the solid-liquid interface. This perspective establishes an indispensable in situ research model for future studies and offers exciting research prospects for scientists and spectroscopists.
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Affiliation(s)
- Jiali Wang
- Department of Chemistry and Center for Emerging Materials and Advanced Devices, National Taiwan University, Taipei, 10617, Taiwan
| | - Chia-Shuo Hsu
- Department of Chemistry and Center for Emerging Materials and Advanced Devices, National Taiwan University, Taipei, 10617, Taiwan
| | - Tai-Sing Wu
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan
| | - Ting-Shan Chan
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan.
| | - Nian-Tzu Suen
- College of Chemistry & Chemical Engineering, Yangzhou University, 225002, Yangzhou, China
| | - Jyh-Fu Lee
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan
| | - Hao Ming Chen
- Department of Chemistry and Center for Emerging Materials and Advanced Devices, National Taiwan University, Taipei, 10617, Taiwan.
- National Synchrotron Radiation Research Center, Hsinchu, 30076, Taiwan.
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, 11031, Taiwan.
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3
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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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4
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Watson L, Rankine CD, Penfold TJ. Beyond structural insight: a deep neural network for the prediction of Pt L 2/3-edge X-ray absorption spectra. Phys Chem Chem Phys 2022; 24:9156-9167. [PMID: 35393987 DOI: 10.1039/d2cp00567k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
X-ray absorption spectroscopy at the L2/3 edge can be used to obtain detailed information about the local electronic and geometric structure of transition metal complexes. By virtue of the dipole selection rules, the transition metal L2/3 edge usually exhibits two distinct spectral regions: (i) the "white line", which is dominated by bound electronic transitions from metal-centred 2p orbitals into unoccupied orbitals with d character; the intensity and shape of this band consequently reflects the d density of states (d-DOS), which is strongly modulated by mixing with ligand orbitals involved in chemical bonding, and (ii) the post-edge, where oscillations encode the local geometric structure around the X-ray absorption site. In this Article, we extend our recently-developed XANESNET deep neural network (DNN) beyond the K-edge to predict X-ray absorption spectra at the Pt L2/3 edge. We demonstrate that XANESNET is able to predict Pt L2/3 -edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in practical situations is demonstrated by application to two Pt complexes, and by simulating the transient spectrum of a photoexcited dimeric Pt complex. Our discussion includes an analysis of the feature importance in our DNN which demonstrates the role of key features and assists with interpreting the performance of the network.
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Affiliation(s)
- Luke Watson
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle, upon Tyne, NE1 7RU, UK.
| | - Conor D Rankine
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle, upon Tyne, NE1 7RU, UK.
| | - Thomas J Penfold
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle, upon Tyne, NE1 7RU, UK.
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5
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Operando Photo-Electrochemical Catalysts Synchrotron Studies. NANOMATERIALS 2022; 12:nano12050839. [PMID: 35269331 PMCID: PMC8912469 DOI: 10.3390/nano12050839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/25/2022] [Accepted: 02/27/2022] [Indexed: 01/27/2023]
Abstract
The attempts to develop efficient methods of solar energy conversion into chemical fuel are ongoing amid climate changes associated with global warming. Photo-electrocatalytic (PEC) water splitting and CO2 reduction reactions show high potential to tackle this challenge. However, the development of economically feasible solutions of PEC solar energy conversion requires novel efficient and stable earth-abundant nanostructured materials. The latter are hardly available without detailed understanding of the local atomic and electronic structure dynamics and mechanisms of the processes occurring during chemical reactions on the catalyst–electrolyte interface. This review considers recent efforts to study photo-electrocatalytic reactions using in situ and operando synchrotron spectroscopies. Particular attention is paid to the operando reaction mechanisms, which were established using X-ray Absorption (XAS) and X-ray Photoelectron (XPS) Spectroscopies. Operando cells that are needed to perform such experiments on synchrotron are covered. Classical and modern theoretical approaches to extract structural information from X-ray Absorption Near-Edge Structure (XANES) spectra are discussed.
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6
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Rosen AS, Notestein JM, Snurr RQ. Realizing the data-driven, computational discovery of metal-organic framework catalysts. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100760] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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7
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Xiang S, Huang P, Li J, Liu Y, Marcella N, Routh PK, Li G, Frenkel AI. Solving the structure of "single-atom" catalysts using machine learning - assisted XANES analysis. Phys Chem Chem Phys 2022; 24:5116-5124. [PMID: 35156671 DOI: 10.1039/d1cp05513e] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
"Single-atom" catalysts (SACs) have demonstrated excellent activity and selectivity in challenging chemical transformations such as photocatalytic CO2 reduction. For heterogeneous photocatalytic SAC systems, it is essential to obtain sufficient information of their structure at the atomic level in order to understand reaction mechanisms. In this work, a SAC was prepared by grafting a molecular cobalt catalyst on a light-absorbing carbon nitride surface. Due to the sensitivity of the X-ray absorption near edge structure (XANES) spectra to subtle variances in the Co SAC structure in reaction conditions, different machine learning (ML) methods, including principal component analysis, K-means clustering, and neural network (NN), were utilized for in situ Co XANES data analysis. As a result, we obtained quantitative structural information of the SAC nearest atomic environment, thereby extending the NN-XANES approach previously demonstrated for nanoparticles and size-selective clusters.
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Affiliation(s)
- Shuting Xiang
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Peipei Huang
- Department of Chemistry, University of New Hampshire, Durham, New Hampshire 03824, USA.
| | - Junying Li
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Yang Liu
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Nicholas Marcella
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Prahlad K Routh
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA.
| | - Gonghu Li
- Department of Chemistry, University of New Hampshire, Durham, New Hampshire 03824, USA.
| | - Anatoly I Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA. .,Chemistry Division, Brookhaven National Laboratory, Upton, New York 11973, USA
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8
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Martini A, Guda AA, Guda SA, Bugaev AL, Safonova OV, Soldatov AV. Machine learning powered by principal component descriptors as the key for sorted structural fit of XANES. Phys Chem Chem Phys 2021; 23:17873-17887. [PMID: 34378592 DOI: 10.1039/d1cp01794b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Modern synchrotron radiation sources and free electron laser made X-ray absorption spectroscopy (XAS) an analytical tool for the structural analysis of materials under in situ or operando conditions. Fourier approach applied to the extended region of the XAS spectrum (EXAFS) allows the estimation of the number of structural and non-structural parameters which can be refined through a fitting procedure. The near edge region of the XAS spectrum (XANES) is also sensitive to the coordinates of all the atoms in the local cluster around the absorbing atom. However, in contrast to EXAFS, the existing approaches of quantitative analysis provide no estimation for the number of structural parameters that can be evaluated for a given XANES spectrum. This problem exists both for the classical gradient descent approaches and for modern machine learning methods based on neural networks. We developed a new approach for rational fit based on principal component descriptors of the spectrum. In this work the principal component analysis (PCA) is applied to a dataset of theoretical spectra calculated a priori on a grid of variable structural parameters of a molecule or cluster. Each principal component of the dataset is related then to a combined variation of several structural parameters, similar to the vibrational normal mode. Orthogonal principal components determine orthogonal deformations that can be extracted independently upon the analysis of the XANES spectrum. Applying statistical criteria, the PCA-based fit of the XANES determines the accessible structural information in the spectrum for a given system.
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Affiliation(s)
- A Martini
- The Smart Materials Research Institute, Southern Federal University, 344090 Sladkova 178/24, Rostov-on-Don, Russia.
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9
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Martini A, Bugaev AL, Guda SA, Guda AA, Priola E, Borfecchia E, Smolders S, Janssens K, De Vos D, Soldatov AV. Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach. J Phys Chem A 2021; 125:7080-7091. [PMID: 34351779 DOI: 10.1021/acs.jpca.1c03746] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - A A Guda
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
| | - 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
| | - E Borfecchia
- Department of Chemistry, University of Torino, Via P. Giuria 7, 10125 Torino, Italy
| | - 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
| | - 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
| | - 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
| | - A V Soldatov
- The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
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10
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Altintas C, Altundal OF, Keskin S, Yildirim R. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation. J Chem Inf Model 2021; 61:2131-2146. [PMID: 33914526 PMCID: PMC8154255 DOI: 10.1021/acs.jcim.1c00191] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Indexed: 02/06/2023]
Abstract
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.
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Affiliation(s)
- Cigdem Altintas
- Department
of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Omer Faruk Altundal
- Department
of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Seda Keskin
- Department
of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Ramazan Yildirim
- Department
of Chemical Engineering, Boğaziçi
University, Bebek, 34342 Istanbul, Turkey
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11
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Madkhali MMM, Rankine CD, Penfold TJ. Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments. Phys Chem Chem Phys 2021; 23:9259-9269. [PMID: 33885072 DOI: 10.1039/d0cp06244h] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a near-infrared (NIR) pulse to rapidly heat a sample, offering an approach for triggering these processes and directly accessing thermally-activated pathways. However, thermal activation inherently increases the disorder of the system under study and, as a consequence, can make quantitative interpretations of structural changes challenging. In this Article, we optimise a deep neural network (DNN) for the instantaneous prediction of Co K-edge X-ray absorption near-edge structure (XANES) spectra. We apply our DNN to analyse T-jump pump/X-ray probe data pertaining to the ligand exchange processes and solvation dynamics of Co2+ in chlorinated aqueous solution. Our analysis is greatly facilitated by machine learning, as our DNN is able to predict quickly and cost-effectively the XANES spectra of thousands of geometric configurations sampled from ab initio molecular dynamics (MD) using nothing more than the local geometric environment around the X-ray absorption site. We identify directly the structural changes following the T-jump, which are dominated by sample heating and a commensurate increase in the Debye-Waller factor.
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Affiliation(s)
- Marwah M M Madkhali
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
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12
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Rankine CD, Penfold TJ. Progress in the Theory of X-ray Spectroscopy: From Quantum Chemistry to Machine Learning and Ultrafast Dynamics. J Phys Chem A 2021; 125:4276-4293. [DOI: 10.1021/acs.jpca.0c11267] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- C. D. Rankine
- Chemistry—School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, U.K
| | - T. J. Penfold
- Chemistry—School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, U.K
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13
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Timoshenko J, Roldan Cuenya B. In Situ/ Operando Electrocatalyst Characterization by X-ray Absorption Spectroscopy. Chem Rev 2021; 121:882-961. [PMID: 32986414 PMCID: PMC7844833 DOI: 10.1021/acs.chemrev.0c00396] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Indexed: 12/18/2022]
Abstract
During the last decades, X-ray absorption spectroscopy (XAS) has become an indispensable method for probing the structure and composition of heterogeneous catalysts, revealing the nature of the active sites and establishing links between structural motifs in a catalyst, local electronic structure, and catalytic properties. Here we discuss the fundamental principles of the XAS method and describe the progress in the instrumentation and data analysis approaches undertaken for deciphering X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectra. Recent usages of XAS in the field of heterogeneous catalysis, with emphasis on examples concerning electrocatalysis, will be presented. The latter is a rapidly developing field with immense industrial applications but also unique challenges in terms of the experimental characterization restrictions and advanced modeling approaches required. This review will highlight the new insight that can be gained with XAS on complex real-world electrocatalysts including their working mechanisms and the dynamic processes taking place in the course of a chemical reaction. More specifically, we will discuss applications of in situ and operando XAS to probe the catalyst's interactions with the environment (support, electrolyte, ligands, adsorbates, reaction products, and intermediates) and its structural, chemical, and electronic transformations as it adapts to the reaction conditions.
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Affiliation(s)
- Janis Timoshenko
- Department of Interface Science, Fritz-Haber Institute of the Max-Planck Society, 14195 Berlin, Germany
| | - Beatriz Roldan Cuenya
- Department of Interface Science, Fritz-Haber Institute of the Max-Planck Society, 14195 Berlin, Germany
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14
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Timoshenko J, Frenkel AI. “Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors. ACS Catal 2019. [DOI: 10.1021/acscatal.9b03599] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Janis Timoshenko
- Department of Interface Science, Fritz-Haber-Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Anatoly I. Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
- Chemistry Division, Brookhaven National Laboratory, Upton, New York 11973, United States
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