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FitIt
FitIt is graphical software to fit X-ray absorption near edge structure (XANES). It can be used to determine the values of local atomic structure parameters on the basis of minimization between theoretical and experimental spectra. It is the program for the fitting and therefore it always uses external programs, for example FEFF8 or FDMNES, for fixed geometry calculations of XANES. In order to minimize the number of such calculations, which can be very time-consuming, multidimensional interpolation algorithm is implemented into the FitIt. Such approach has allowed also to develop visual control of the fitting procedure and it is possible to vary structural parameters by sliders and immediately see the theoretical spectrum corresponding to these structural parameters.C. Battocchio, F. D’Acapito, G. Smolentsev, A.V. Soldatov, I. Fratoddi, G. Contini, I. Davoli, G. Polzonetti and S. Mobilio, ''XAS study of a Pt-containing rod-like organometallic polymer''Chem. Phys. 325, 422 (2006)/ref ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
XANES
X-ray absorption near edge structure (XANES), also known as near edge X-ray absorption fine structure (NEXAFS), is a type of absorption spectroscopy that indicates the features in the X-ray absorption spectra ( XAS) of condensed matter due to the photoabsorption cross section for electronic transitions from an atomic core level to final states in the energy region of 50–100 eV above the selected atomic core level ionization energy, where the wavelength of the photoelectron is larger than the interatomic distance between the absorbing atom and its first neighbour atoms. Terminology Both XANES and NEXAFS are acceptable terms for the same technique. XANES name was invented in 1980 by Antonio Bianconi to indicate strong absorption peaks in X-ray absorption spectra in condensed matter due to multiple scattering resonances above the ionization energy. The name NEXAFS was introduced in 1983 by Jo Stohr and is synonymous with XANES, but is generally used when applied to surface and molec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
FEFF8
FEFF is a software program used in x-ray absorption spectroscopy. It contains self-consistent space, real space multiple-scattering code for simultaneous calculations of x-ray-Absorption spectrum, absorption spectra and electronic structure. Output includes extended x-ray-absorption fine structure (EXAFS), full multiple scattering calculations of various x-ray absorption spectra (X-ray absorption spectroscopy, XAS) and projected local densities of states (Local density of states, LDOS). The spectra include x-ray absorption near edge structure (XANES), x-ray natural circular dichroism (XNCD), and non-resonant x-ray emission spectra. Calculations of the x-ray scattering amplitude (Thomson scattering, Thomson and anomalous parts) and spin dependent calculations of x-ray magnetic circular dichroism (XMCD) and Spin polarization, spin polarized x-ray absorption spectra (SPXAS and SPEXAFS) are also possible, but less automated. The most recent version of FEFF is FEFF10, released in 2020. U ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
FDMNES
The FDMNES program calculates the spectra of different spectroscopies related to the real or virtual absorption of x-ray in material. It gives the absorption cross sections of photons around the ionization edge, that is in the energy range of XANES. The calculation is performed with all conditions of rectilinear or circular polarization. In the same way, it calculates the structure factors and intensities of anomalous or resonant diffraction spectra (DAFS or RXS). The code uses two techniques of monoelectronic calculations. The first one is based on the Finite Difference Method ( FDM) to solve the Schrödinger equation. In that way the shape of the potential is free and in particular avoid the muffin-tin approximation. The second one uses the Green formalism (multiple scattering) on a muffin- tin potential. This approach can be less precise but is faster. FDMNES is used as external program to calculate basic spectra for XANES fitting using FitIt. It can also be used to calculat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal Component Analysis
Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
