Improving sensitivity of XANES structural fit to the bridged metal-metal coordination

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.

Tracking Coordination Environment and Reaction Intermediates in Homogeneous and Heterogeneous Epoxidation Catalysts via Ti L2,3-Edge Near-Edge X-ray Absorption Fine Structures

Ti-based molecules and materials are ubiquitous and play a major role in both homogeneous and heterogeneous catalytic processes. Understanding the electronic structures of their active sites (oxidation state, local symmetry, and ligand environment) is key to developing molecular-level structure-property relationships. In that context, X-ray absorption spectroscopy (XAS) offers a unique combination of elemental selectivity and sensitivity to local symmetry. Commonly, for early transition metals such as Ti, K-edge XAS is applied for in situ characterization and subsequent structural analysis with high sensitivity toward tetrahedral species. Ti L2,3-edge spectroscopy is in principle complementary and offers specific opportunities to interrogate the electronic structure of five-and six-coordinated species. It is, however, much more rarely implemented because the use of soft X-rays implies ultrahigh vacuum conditions. Furthermore, the interpretation of the data can be challenging. Here, we show how Ti L2,3-edge spectroscopy can help to obtain unique information about both homogeneous and heterogeneous epoxidation catalysts and develop a molecular-level relationship between spectroscopic signatures and electronic structures. Toward this goal, we first establish a spectral library of molecular Ti reference compounds, comprising various coordination environments with mono- and dimeric Ti species having O, N, and Cl ligands. We next implemented a computational methodology based on multiplet ligand field theory and maximally localized Wannier orbitals benchmarked on our library to understand Ti L2,3-edge spectroscopic signatures. We finally used this approach to track and predict the spectra of catalytically relevant intermediates, focusing on Ti-based olefin epoxidation catalysts.

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set. A classification problem was solved to predict the presence or absence of possible tilings and secondary building units (SBUs). Several natural tilings and SBUs were also predicted with an accuracy above 89%. The set of continuous descriptors was also suggested, and the regression problem was also solved using the ExtraTrees algorithm. For the latter problem, additional IR spectra were computed for the structures with artificially modified cell parameters, expanding the database to 470 different spectra of zeolites. The resulting prediction quality above or close to 90% was obtained for the average Si-O distances, Si-O-Si angles, and volume of TO₄ tetrahedra. The obtained results provide new possibilities for utilization of infrared spectra as a quantitative tool for characterization of zeolites.

Development of a ReaxFF potential for Au-Pd

The bimetallic alloys often outperform their single-component counterparts due to synergistic effects. Being widely known, the Au-Pd alloy is a promising candidate for the novel heterogeneous nanocatalysts. Rational design of such systems requires theoretical simulations under ambient conditions.Ab initioquantum-mechanical calculations employ the density functional theory (DFT) and are limited to the systems with few tens of atoms and short timescales. The alternative solution implies development of reliable atomistic potentials. Among different approaches ReaxFF combines chemical accuracy and low computational costs. However, the development of a new potential is a problem without unique solution and thus requires accurate validation criteria. In this work we construct ReaxFF potential for the Au-Pd system based onab initioDFT calculations for bulk structures, slabs and nanoparticles with different stoichiometry. The validation was performed with molecular dynamics and Monte-Carlo calculations. We present several optimal parametrizations that describe experimental bulk mechanical and thermal properties, atomic order-disorder phase transition temperatures and the resulting ordered crystal structures.

Machine learning powered by principal component descriptors as the key for sorted structural fit of XANES

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.

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

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.

Deciphering the Phillips Catalyst by Orbital Analysis and Supervised Machine Learning from Cr Pre-edge XANES of Molecular Libraries

Unveiling the nature and the distribution of surface sites in heterogeneous catalysts, and for the Phillips catalyst (CrO₃/SiO₂) in particular, is still a grand challenge despite more than 60 years of research. Commonly used references in Cr K-edge XANES spectral analysis rely on bulk materials (Cr-foil, Cr₂O₃) or molecules (CrCl₃) that significantly differ from actual surface sites. In this work, we built a library of Cr K-edge XANES spectra for a series of tailored molecular Cr complexes, varying in oxidation state, local coordination environment, and ligand strength. Quantitative analysis of the pre-edge region revealed the origin of the pre-edge shape and intensity distribution. In particular, the characteristic pre-edge splitting observed for Cr(III) and Cr(IV) molecular complexes is directly related to the electronic exchange interactions in the frontier orbitals (spin-up and -down transitions). The series of experimental references was extended by theoretical spectra for potential active site structures and used for training the Extra Trees machine learning algorithm. The most informative features of the spectra (descriptors) were selected for the prediction of Cr oxidation states, mean interatomic distances in the first coordination sphere, and type of ligands. This set of descriptors was applied to uncover the site distribution in the Phillips catalyst at three different stages of the process. The freshly calcined catalyst consists of mainly Cr(VI) sites. The CO-exposed catalyst contains mainly Cr(II) silicates with a minor fraction of Cr(III) sites. The Phillips catalyst exposed to ethylene contains mainly highly coordinated Cr(III) silicates along with unreduced Cr(VI) sites.

Crystal Orbital Overlap Population and X-ray Absorption Spectroscopy

We present an extension of an ab initio numerical tool calculating X-ray absorption spectroscopies and crystal orbital overlap populations at the same time. Density functional theory is used to calculate the electronic structure in both occupied (valence to core X-ray emission spectroscopy) and nonoccupied states (i.e., X-ray absorption near the edge structure and X-ray magnetic circular dichroism) and to evaluate the orbital overlap typifying the covalency between neighboring atoms. We show how the different features in the experimental spectra can be correlated to the chemical bonds around the absorbing atoms in several examples including acrylonitrile molecules, rutile TiO₂, Li₂RuO₃ high-energy density cathode, ZnO, and anti-ferromagnetic V₂O₃.

Optimized Finite Difference Method for the Full-Potential XANES Simulations: Application to Molecular Adsorption Geometries in MOFs and Metal-Ligand Intersystem Crossing Transients

Accurate modeling of the X-ray absorption near-edge spectra (XANES) is required to unravel the local structure of metal sites in complex systems and their structural changes upon chemical or light stimuli. Two relevant examples are reported here concerning the following: (i) the effect of molecular adsorption on 3d metals hosted inside metal-organic frameworks and (ii) light induced dynamics of spin crossover in metal-organic complexes. In both cases, the amount of structural models for simulation can reach a hundred, depending on the number of structural parameters. Thus, the choice of an accurate but computationally demanding finite difference method for the ab initio X-ray absorption simulations severely restricts the range of molecular systems that can be analyzed by personal computers. Employing the FDMNES code [Phys. Rev. B, 2001, 63, 125120] we show that this problem can be handled if a proper diagonalization scheme is applied. Due to the use of dedicated solvers for sparse matrices, the calculation time was reduced by more than 1 order of magnitude compared to the standard Gaussian method, while the amount of required RAM was halved. Ni K-edge XANES simulations performed by the accelerated version of the code allowed analyzing the coordination geometry of CO and NO on the Ni active sites in CPO-27-Ni MOF. The Ni-CO configuration was found to be linear, while Ni-NO was bent by almost 90°. Modeling of the Fe K-edge XANES of photoexcited aqueous [Fe(bpy)3](2+) with a 100 ps delay we identified the Fe-N distance elongation and bipyridine rotation upon transition from the initial low-spin to the final high-spin state. Subsequently, the X-ray absorption spectrum for the intermediate triplet state with expected 100 fs lifetime was theoretically predicted.