scholarly journals Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy

2020 ◽  
Vol 22 (34) ◽  
pp. 18902-18910 ◽  
Author(s):  
Nicholas Marcella ◽  
Yang Liu ◽  
Janis Timoshenko ◽  
Erjia Guan ◽  
Mathilde Luneau ◽  
...  
Keyword(s):  
Neural Network ◽  
Local Structure ◽  
Bimetallic Nanoparticles ◽  
Edge Structure ◽  
X Ray ◽  
Coordination Numbers ◽  
Rich Information ◽  
Bimetallic Nanocatalysts ◽  
Assisted Analysis ◽  
X Ray Absorption

Trained neural networks are used to extract the first partial coordination numbers from XANES spectra. In bimetallic nanoparticles, the four local structure descriptors provide rich information on structural motifs.

scholarly journals Solving local structure around dopants in metal nanoparticles with ab initio modeling of X-ray absorption near edge structure

2016 ◽  
Vol 18 (29) ◽  
pp. 19621-19630 ◽  
Author(s):  
Janis Timoshenko ◽  
Atal Shivhare ◽  
Robert W. J. Scott ◽  
Deyu Lu ◽  
Anatoly I. Frenkel
Keyword(s):  
Ab Initio ◽  
Metal Nanoparticles ◽  
Local Structure ◽  
Heterogeneous Catalysts ◽  
Edge Structure ◽  
X Ray ◽  
Metal Impurities ◽  
Local Environments ◽  
X Ray Absorption ◽  
Xanes Analysis

XANES analysis guided by ab initio modeling is proposed for refinement of local environments around metal impurities in heterogeneous catalysts.

scholarly journals A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra

2020 ◽  
Author(s):  
Conor Rankine ◽  
Marwah Madkhali ◽  
Thomas Penfold
Keyword(s):  
Neural Network ◽  
Absorption Spectra ◽  
Deep Neural Network ◽  
Theoretical Calculations ◽  
End Users ◽  
Supervised Machine Learning ◽  
Strong Impact ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

<p>X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. <i>in operando</i> catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less </p><p>than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus.</p>

scholarly journals The Role of Structural Representation in the Performance of a Deep Neural Network for X-ray Spectroscopy

Molecules ◽  
2020 ◽  
Vol 25 (11) ◽  
pp. 2715
Author(s):  
Marwah M.M. Madkhali ◽  
Conor D. Rankine ◽  
Thomas J. Penfold
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Chemical Space ◽  
Theoretical Calculations ◽  
Local Environment ◽  
Structural Representation ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

An important consideration when developing a deep neural network (DNN) for the prediction of molecular properties is the representation of the chemical space. Herein we explore the effect of the representation on the performance of our DNN engineered to predict Fe K-edge X-ray absorption near-edge structure (XANES) spectra, and address the question: How important is the choice of representation for the local environment around an arbitrary Fe absorption site? Using two popular representations of chemical space—the Coulomb matrix (CM) and pair-distribution/radial distribution curve (RDC)—we investigate the effect that the choice of representation has on the performance of our DNN. While CM and RDC featurisation are demonstrably robust descriptors, it is possible to obtain a smaller mean squared error (MSE) between the target and estimated XANES spectra when using RDC featurisation, and converge to this state a) faster and b) using fewer data samples. This is advantageous for future extension of our DNN to other X-ray absorption edges, and for reoptimisation of our DNN to reproduce results from higher levels of theory. In the latter case, dataset sizes will be limited more strongly by the resource-intensive nature of the underlying theoretical calculations.

Local Structure of Pr, Nd, and Sm Complex Oxides and Their X-ray Absorption Near Edge Structure Spectra

2014 ◽  
Vol 118 (36) ◽  
pp. 20881-20888 ◽  
Author(s):  
Hiroyuki Asakura ◽  
Tetsuya Shishido ◽  
Shingo Fuchi ◽  
Kentaro Teramura ◽  
Tsunehiro Tanaka
Keyword(s):  
Local Structure ◽  
Complex Oxides ◽  
Edge Structure ◽  
X Ray ◽  
X Ray Absorption

Novel Fe-Pd/SiO2 catalytic materials for degradation of chlorinated organic compounds in water

2014 ◽  
Vol 86 (7) ◽  
pp. 1141-1158 ◽  
Author(s):  
Leonid M. Kustov ◽  
Souhail R. Al-Abed ◽  
Jurate Virkutyte ◽  
Olga A. Kirichenko ◽  
Elena V. Shuvalova ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Photoelectron Spectroscopy ◽  
Bimetallic Nanoparticles ◽  
Ambient Conditions ◽  
Chlorinated Organic Compounds ◽  
Edge Structure ◽  
X Ray ◽  
Highly Dispersed ◽  
Chlorinated Organic ◽  
X Ray Absorption

AbstractNovel reactive materials for catalytic degradation of chlorinated organic compounds in water at ambient conditions have been prepared on the basis of silica-supported Pd-Fe nanoparticles. Nanoscale Fe-Pd particles were synthesized inside porous silica supports using (NH4)3[Fe(C2O4)3] and [Pd(NH3)4]Cl2 or Pd acetate as reaction precursors. According to temperature programmed reduction (TPR) studies, Pd introduction decreased the reduction temperature of the supported Fen+ species and nearly complete reduction with H2 was observed at 400 °C. The successful surface loading with Pd was confirmed by X-ray photoelectron spectroscopy (XPS) analysis. Characterization of the samples by X-ray diffraction (XRD) and X-ray absorption near-edge structure + extended X-ray absorption fine structure (XANES + EXAFS) verified the presence of highly dispersed Pd0, Pdx Fe1–x and Fe0 phases. Reduction of the supported precursors in hydrogen resulted in materials that were highly active in perchloroethene (PCE) degradation and 2-chlorobiphenyl (2-ClBP) dechlorination. It was found that highly dispersed amorphous Fe-Pd bimetallic nanoparticles on silica support showed superior catalytic activity against PCE dechlorination in comparison to the free-standing Fe-Pd nanoparticles. For the samples with the same Fe content, the conversion of chlorinated organics as well as the stability increased with the Pd loading, e.g., the most effective degradation of PCEs and 2-ClBP was achieved at a Pd loading of 2.3–3.2 wt. %.

Ab Initio XAFS and XANES Standards

1993 ◽  
Vol 307 ◽  
Author(s):  
J. J. Rehr ◽  
S. I. Zabinsky ◽  
R. C. Albers
Keyword(s):  
Fine Structure ◽  
Ab Initio ◽  
Multiple Scattering ◽  
State Of The Art ◽  
Experimental Measurements ◽  
Edge Structure ◽  
X Ray ◽  
Coordination Numbers ◽  
X Ray Absorption ◽  
Better Than

ABSTRACTAb initio x-ray-absorption fine structure (XAFS) and x-ray-absorption near edge structure (XANES) standards are developed for molecules and solids. These standard XAFS spectra are obtained from ab initio XAFS calculations, using an automated code, FEFF, which we have extended to include multiple-scattering (MS) and XANES calculations. Our treatment is based on state-of-the-art curved-wave MS theory. Sample results are presented and compared with experiment. We find that these theoretical standards are comparable with experimental measurements, yielding distance determinations better than 0.02 Å and coordination numbers better than 20%. Our approach also gives a new MS interpretation of the σ* shape-resonances in XANES.

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