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)32+ 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.
X-ray absorption near-edge spectroscopy (XANES) is becoming an extremely popular tool for material science thanks to the development of new synchrotron radiation light sources. It provides ...information about charge state and local geometry around atoms of interest in operando and extreme conditions. However, in contrast to X-ray diffraction, a quantitative analysis of XANES spectra is rarely performed in the research papers. The reason must be found in the larger amount of time required for the calculation of a single spectrum compared to a diffractogram. For such time-consuming calculations, in the space of several structural parameters, we developed an interpolation approach proposed originally by Smolentsev and Soldatov (2007). The current version of this software, named PyFitIt, is a major upgrade version of FitIt and it is based on machine learning algorithms. We have chosen Jupyter Notebook framework to be friendly for users and at the same time being available for remastering. The analytical work is divided into two steps. First, the series of experimental spectra are analyzed statistically and decomposed into principal components. Second, pure spectral profiles, recovered by principal components, are fitted by theoretical interpolated spectra. We implemented different schemes of choice of nodes for approximation and learning algorithms including Gradient Boosting of Random Trees, Radial Basis Functions and Neural Networks. The fitting procedure can be performed both for a XANES spectrum or for a difference spectrum, thus minimizing the systematic errors of theoretical simulations. The problem of several local minima is addressed in the framework of direct and indirect approaches.
Program title: PyFitIt.
Program Files doi:http://dx.doi.org/10.17632/ydkgfdc38t.1
Licensing provisions: GNU General Public License 3.
Programming language: Python, Jupyter Notebook framework.
Nature of problem: Quantitative structural refinements of the X-ray absorption near-edge structure spectra (XANES). Identification of the pure spectral and concentration profiles associated with an experimental XANES dataset.
Solution method: The fitting procedure of the experimental XANES spectra or of their differences is realized by means of the inverse and direct approaches based on the training set and approximation machine learning algorithms. The spectral resolution method is based on the PCA technique involving the usage of a target transformation matrix.
Additional comments including restrictions and unusual features: The current version is compatible with the free FDMNES program package for XANES simulations. However, users can prepare their own matrices of spectra calculated by an arbitrary software and the corresponding structural parameters to perform the fitting procedure in PyFitIt. The complete set of examples is distributed along with the program.
References: PyFitIt web page: http://hpc.nano.sfedu.ru/pyfitit/
Surface plasmon resonance is sensitive to the size and shape of gold nanoparticles. The quantitative analysis of the ultraviolet–visible spectra provides information about the structural parameters ...of the nanoparticles. This task is related to the inverse design problem where machine learning (ML) algorithms show superior performance over classical approaches for problems with many degrees of freedom. If a ML algorithm is used as a black box, it often fails when target experimental data have systematic differences with the theoretical training data set. Our work aims to assess the uncertainties in the structural analysis of gold nanoparticles performed using optical spectroscopy. Therefore, ML is trained over a theoretical data set and then used as a tool to predict the spectrum for any combination of structural parameters. The region of a feasible solution is analyzed via L2 norm contour plots, and the method is extended to multicomponent mixtures where Gaussian distribution mimics the particle size distribution. We also demonstrate that the ML algorithm is able to select only informative features of the spectrum (descriptors) and establish an analytical relation between descriptors of spectra and structural parameters. This work extends the capabilities of optical spectroscopy as an analytical tool for noble metal nanoparticles.
RNA velocity has enabled the recovery of directed dynamic information from single‐cell transcriptomics by connecting measurements to the underlying kinetics of gene expression. This approach has ...opened up new ways of studying cellular dynamics. Here, we review the current state of RNA velocity modeling approaches, discuss various examples illustrating limitations and potential pitfalls, and provide guidance on how the ensuing challenges may be addressed. We then outline future directions on how to generalize the concept of RNA velocity to a wider variety of biological systems and modalities.
This Review discusses the emerging challenges and potential pitfalls of current RNA velocity modeling approaches and provides guidance on how to address them.
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)32 complexes.
In the unit disc, we consider the Dirichlet problem for second order elliptic system with only higher order constant coefficients. We establish the unique solvability of this problem under the ...assumption that the problem is Fredholm, and obtain an explicit formula for the solution.
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•Different theoretical methods in the simulation of the XANES spectra are discussed.•An extended list of available codes for XANES spectra simulation is provided.•The potential of ...operando XANES in catalysis is described with relevant examples.•Chemometric methods in the treatment of operando XANES spectra is discussed.•Machine learning approaches are used to provide structural determination from XANES.
In the last decade the appearance of progressively more sophisticated codes, together with the increased computational capabilities, has made XANES a spectroscopic technique able to quantitatively confirm (or discard) a structural model, thus becoming a new fundamental diagnostic tool in catalysis, where the active species are often diluted metal centers supported on a matrix. After providing a brief historical introduction and the basic insights on the technique, in this review article, we provide a selection of four examples where operando XANES technique has been able to provide capital information on the structure of the active site in catalysts of industrial relevance: (i) Phillips catalyst for ethylene polymerization reaction; (ii) TS-1 catalyst for selective hydrogenation reactions; (iii) carbon supported Pd nanoparticles for hydrogenation reactions; (iv) Cu-CHA zeolite for NH3-assisted selective reduction of NOx and for partial oxidation of methane to methanol. The last example testifies how the multivariate curve resolution supported by the alternating least-squares algorithm applied to a high number of XANES spectra collected under operando conditions allows to quantitatively determine different species in mutual transformation. This approach is particularly powerful in the analysis of experiments where a large number of spectra has been collected, typical of time- or space-resolved experiments. Finally, machine learning approaches (both indirect and direct) have been applied to determine, from the XANES spectra, the structure of CO, CO2 and NO adsorbed on Ni2+ sites of activated CPO-27-Ni metal-organic framework.
Cu-SSZ-13 is a highly active NH
-SCR catalyst for the abatement of harmful nitrogen oxides (NO
,
= 1, 2) from the exhausts of lean-burn engines. The study of Cu-speciation occurring upon thermal ...dehydration is a key step for the understanding of the enhanced catalytic properties of this material and for identifying the SCR active sites and their redox capability. Herein, we combined FTIR, X-ray absorption (XAS) and emission (XES) spectroscopies with DFT computational analysis to elucidate the nature and location of the most abundant Cu sites in the activated catalyst. Different Cu species have been found to be dominant as a function of the dehydration temperature and conditions. Data analysis revealed that the dehydration process of Cu cations is essentially completed at 250 °C, with the formation of dehydrated CuOH
species hosted in close proximity to 1-Al sites in both d6r and 8r units of the SSZ-13 matrix. These species persist at higher temperatures only if a certain amount of O
is present in the gas feed, while under inert conditions they undergo virtually total "self-reduction" as a consequence of an OH extra-ligand loss, resulting in bi-coordinated bare Cu
cations. Synchrotron characterization supported by computational analysis allowed an unprecedented quantitative refinement of the local environment and structural parameters of these Cu(ii) and Cu(i) species.
A set of Sm-doped iron oxide magnetic nanoparticles (Fe x O y NPs) of different sizes as an example of ferromagnetic NPs at room temperature was synthesized by microware-assisted methods. Powder ...X-ray diffraction and transmission electron microscopy showed Fe x O y NPs with an inverse spinel structure. Mössbauer and X-ray absorption spectroscopy (XAS) were used to study the local atomic and electronic structure of iron in the NPs. Linear combination of XAS spectra of reference iron oxides failed in reproducing the spectra of Fe x O y NPs Piquer, C. ; J. Phys. Chem. C 2014, 118, 1332−1346 . We attribute this fact to the nonstoichiometric distribution of tetrahedral, Td 3+, and octahedral, Oh 3+ and Oh 2+, sites in NPs compared to bulk references. This distribution was successfully reproduced by linear combination of theoretical XAS spectra obtained for clusters where iron was in Td 3+, Oh 3+, and Oh 2+ defined oxidation and symmetry states. This approach allowed us to obtain the quantitative speciation of the fraction of Td 3+, Oh 3+, and Oh 2+ sites as a function of the different size of the Sm-doped Fe x O y NPs based on the XAS data. In contrast to the standard XAS reference compounds analysis where experimental spectra fits are limited to constant Td 3+/Oh 3+ and Td 3+/Oh 3+/Oh 2+ ratios and to Mössbauer spectroscopy that requires liquid helium temperatures, the proposed method opens a new possibility to quantitatively estimate the amount of each independent inequivalent iron site in terms of symmetry and oxidation state at room temperature.
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