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

Machine Learning for Quantitative Structural Information from Infrared Spectra: The Case of Palladium Hydride

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.

Structural Analysis of Single-Atom Catalysts by X-ray Absorption Spectroscopy

ConspectusMetal nanoparticles (NPs) are one of the most frequently used heterogeneous catalysts. However, only the surface atoms in the NPs can participate in catalytic reactions. To maximize the atomic efficiency, the active sites can be reduced to single atoms. Generally, catalysts that have isolated metal atoms on the surface of a support are called single-atom catalysts (SACs). Many techniques have been developed and applied to probe the structures of SACs. Nevertheless, the structural characterization of SACs is still challenging as it requires the analysis of their structure and properties with atomic and sometimes even subatomic resolution. X-ray absorption spectroscopy (XAS) is a powerful tool in investigating the local coordination environment of SACs since it is element-specific and can provide accurate structural information at the subatomic level (∼0.01 Å).In this Account, we present our perspectives on the structural analysis of SACs from some unique features in the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We first highlight the importance of the XANES peak features in the sensitive analysis of SAC structures. Such analysis is illustrated to be even more useful in the joint applications of experimental and theoretical XAS. The inspection of the metal-metal (M-M) peak in Fourier transformed EXAFS (FT-EXAFS) spectra is a widely used method to identify the single-atom structure, but this method is not always reliable. Thus, we point out the importance of fitting EXAFS and the thorough interpretation of structural parameters such as coordination numbers (CNs, the number of neighboring atoms next to a chosen atom), bond distances, and the Debye-Waller factor (σ2). The small FT-EXAFS peak for the M-M shell is often ignored in the structural analysis of SACs. Here, it is demonstrated that a careful analysis of these small peaks could help more reliably analyze the SAC structure, and it would be particularly useful in the analysis of a single-atom alloy (SAA). Next, the usefulness of bond distance and σ2 analysis is highlighted, and such analysis is shown to be particularly helpful for the analysis of SAAs, which is rarely discussed in the literature. Given the advantage that XAS data can be collected under various conditions, we show that in situ XAS can provide important information about the catalytic mechanism of the SAC catalyst. In particular, we emphasize the significance of using an advanced in situ technique to extract detailed structural information that is difficult to obtain from regular XAS experiments. Finally, we highlight the importance of jointly using XAS with other complementary methods in a more complete understanding of the structure and properties of SACs. It is anticipated that with further development of XAS techniques and improved data analysis, XAS will become even more powerful in providing insights into the structure-property relationships of SACs, which can advance their practical applications.

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization

The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material's electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents. However, this approach requires the creation of a special environment that provides a reliable level of response to the agent's actions. As the physical experimental environment of nanocatalyst diagnostics is potentially a complex multiscale system, there are no unified comprehensive representations that formalize the structure and states as a single digital model. This study proposes an approach based on the decomposition of the experimental system into the original physically plausible nodes, with subsequent merging and optimization as a metagraphic representation with which to model the complex multiscale physicochemical environments. The advantage of this approach is the possibility to directly use the numerical model to predict the system states and to optimize the experimental conditions and parameters. Additionally, the obtained model can form the basic planning principles and allow for the optimization of the search for the optimal strategy with which to control the experiment when it is used as a training environment to provide different abstraction levels of system state reactions.

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

Cu-exchanged chabazite is the catalyst of choice for NO_x abatement in diesel vehicles aftertreatment systems via ammonia-assisted selective catalytic reduction (NH₃-SCR). Herein, we exploit in situ X-ray absorption spectroscopy powered by wavelet transform analysis and machine learning-assisted fitting to assess the impact of the zeolite composition on NH₃-mobilized Cu-complexes formed during the reduction and oxidation half-cycles in NH₃-SCR at 200 °C. Comparatively analyzing well-characterized Cu-CHA catalysts, we show that the Si/Al ratio of the zeolite host affects the structure of mobile dicopper(II) complexes formed during the oxidation of the [Cu^I(NH₃)₂]⁺ complexes by O₂. Al-rich zeolites promote a planar coordination motif with longer Cu-Cu interatomic distances, while at higher Si/Al values, a bent motif with shorter internuclear separations is also observed. This is paralleled by a more efficient oxidation at a given volumetric Cu density at lower Si/Al, beneficial for the NO_x conversion under NH₃-SCR conditions at 200 °C.

Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network

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.

Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites

The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.

Operando Photo-Electrochemical Catalysts Synchrotron Studies

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.

Evolution of the active species of homogeneous Ru hydrodeoxygenation catalysts in ionic liquids

This work establishes structure–property relationships in Ru-based catalytic systems for selective hydrodeoxygenation of ketones to alkenes by combining extensive catalytic testing, in situ X-ray absorption spectroscopy (XAS) under high pressures and temperatures and ex situ XAS structural characterization supported by density functional theory (DFT) calculations. Catalytic tests revealed the difference in hydrogenation selectivity for ketones (exemplified by acetone) or alkenes (exemplified by propene) upon changing the reaction conditions, more specifically in the presence of CO during a pretreatment step. XAS data demonstrated the evolution of the local ruthenium structure with different amounts of Cl/Br and CO ligands. In addition, in the absence of CO, the catalyst was reduced to Ru⁰, and this was associated with a significant decrease of the selectivity for ketone hydrogenation. For the Ru–bromide carbonyl complex, selectivity towards acetone hydrogenation over propene hydrogenation was explained on the basis of different relative energies of the first intermediate states of each reaction. These results give a complete understanding of the evolution of the Ru species, used for the catalytic valorization of biobased polyols to olefins in ionic liquids, identifying the undesired deactivation routes as well as possibilities for reactivation.

This work establishes structure–property relationships in Ru-based catalytic systems for the selective hydrodeoxygenation of ketones to alkenes.