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The lack of theoretical understanding of X-Ray Absorption Near Edge Structure (XANES) spectroscopy makes the development of analysis tools for its study a necessity. Here, an algorithm for judicious choice of local minima and maxima points of XANES spectrum (experimental or simulated) is proposed, without any loss of information on peaks location nor on peak strength. We call it the Rising Sun Envelope Method, since it is based on successive regularizations of the spectral measurement that, according to parameter choices that are intrinsic to the measurements, keep peaks location and strength as invariants. This is the first method that finds peaks in XANES automatically, without depending on first derivative information. Nevertheless, a direct computation of Absorption-Edge is provided, where we avoid the issue inflection point computations based on the XANES second derivative, dealing instead with simpler computations of inflection points of higher quality cubic spline approximation. Besides applications of the algorithm to XANES, we illustrate further applications in Electron Energy Loss Spectroscopy (EELS) and Raman spectra., Comment: 10 pages, 12 figures

We developed a method for measuring the similarity between materials, focusing on specific physical properties. The obtained information can be utilized to understand the underlying mechanisms and to support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on non-linear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three datasets of well-characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, we focus on the formation energy, lattice parameter, and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpreting the mechanism, and an improvement of regression models is introduced for predicting the physical properties of the materials. Our experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials physical properties can be significantly increased, confirming the rationality of the proposed similarity measure., Comment: 13 pages

Hydrogen adsorption on graphene-supported metal clusters has brought much controversy due to the complex nature of the bonding between hydrogen and metal clusters. The bond types of hydrogen and graphene-supported Ti clusters are experimentally and theoretically investigated. Transmission electron microscopy shows that Ti clusters of nanometer-size are formed on graphene. Thermal desorption spectroscopy captures three hydrogen desorption peaks from hydrogenated graphene-supported Ti clusters. First principle calculations also found three types of interaction: Two types of bonds with different partial ionic character and physisorption. The physical origin for this rests on the charge state of the Ti clusters: when Ti clusters are neutral, H2 is dissociated, and H forms bonds with the Ti cluster. On the other hand, H2 is adsorbed in molecular form on positively charged Ti clusters, resulting in physisorption. Thus, this work clarifies the bonding mechanisms of hydrogen on graphene-supported Ti clusters.