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.

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.

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.

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.