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We apply a recently developed technique utilizing machine learning for statistical analysis of computational nitrogen K-edge spectra of aqueous triglycine. This method, the emulator-based component analysis, identifies spectrally relevant structural degrees of freedom from a data set filtering irrelevant ones out. Thus tremendous reduction in the dimensionality of the ill-posed nonlinear inverse problem of spectrum interpretation is achieved. Structural and spectral variation across the sampled phase space is notable. Using these data, we train a neural network to predict the intensities of spectral regions of interest from the structure. These regions are defined by the temperature-difference profile of the simulated spectra, and the analysis yields a structural interpretation for their behavior. Even though the utilized local many-body tensor representation implicitly encodes the secondary structure of the peptide, our approach proves that this information is irrecoverable from the spectra. A hard X-ray Raman scattering experiment confirms the overall sensibility of the simulated spectra, but the predicted temperature-dependent effects therein remain beyond the achieved statistical confidence level.

We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO$_2$ at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the K$\beta''$ and K$\beta_2$ peaks in the spectrum from the Coulomb matrix descriptor with a training set of $\sim 10^4$ samples. Spectral-significance-guided dimensionality reduction techniques allow us to construct an approximate inverse mapping from spectral moments to pseudo-Coulomb matrices. When applying this to the moments of the ensemble-mean spectrum, we obtain distances from the active site that match closely to those of the ensemble mean and which moreover reproduce the pressure-induced coordination change in amorphous GeO$_2$. With this approach utilizing emulator-based component analysis, we are able to filter out the artificially complete structural information available from simulated snapshots, and quantitatively analyse structural changes that can be inferred from the changes in the K$\beta$ emission spectrum alone.

We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least squares fitting and the observed behavior is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.

We characterize experimentally and theoretically the high-energy dielectric screening properties of the prototypical correlated metal SrVO$_3$. The dynamical structure factor measured by inelastic x-ray scattering spectroscopy as a function of momentum transfer is in very good agreement with first-principles calculations in the adiabatic local density approximation to time-dependent density-functional theory. Our results reveal the crucial importance of crystal local fields in the charge response function of correlated materials: They lead to depolarization effects for localised excitations and couple spectra from different Brillouin zones.

Electronic transitions involving core-level orbitals offer a localized, atomic-site and element specific peek window into statistical systems such as molecular liquids. Although formally understood, the complex relation between structure and spectrum -- and the effect of statistical averaging of highly differing spectra of individual structures -- render the analysis of an ensemble-averaged core-level spectrum complicated. We explore the applicability of machine learning for molecular structure -- core-level spectrum interpretation. We focus on the electronic Hamiltonian using the \ce{H2O} molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We find predicting spectra easier than predicting structures, where a tighter grid (even unphysical) of the spectrum improves prediction, possibly inviting for over-interpretation of the model. The accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space, which can not be overcome by model selection based on generalizability.

Developing characterization techniques and analysis methods adapted to the investigation of nanoparticles (NPs) is of fundamental importance considering the role of these materials in many fields of research. The study of actinide based NPs, despite their environmental relevance, is still underdeveloped compared to that of NPs based on stable and lighter elements. We present here an investigation of ThO2 NPs performed with High-Energy Resolution Fluorescence Detected (HERFD) X-ray Absorption Near-Edge Structure (XANES) and with ab initio XANES simulations. The first post-edge feature of Th L3 edge HERFD XANES disappears in small NPs and simulations considering non-relaxed structural models reproduce the trends observed in experimental data. Inspection of the simulations from Th atoms in the core and on the surface of the NP indeed demonstrates that the the first post-edge feature is very sensitive to the lowering of the number of coordinating atoms and therefore to the more exposed Th atoms at the surface of the NP. The sensitivity of the L3 edge HERFD XANES to low coordinated atoms at the surface stems from the hybridization of the d-Density of States (DOS) of Th with both O and Th neighboring atoms. This may be a common feature to other oxide systems that can be exploited to investigate surface interactions.

We present an X-ray Compton scattering study on aqueous Trimethylamine N-oxide (TMAO) and guanidine hydrochloride solutions (GdnHCl) as a function of temperature. Independent from the concentration of the solvent, Compton profiles almost resemble results for liquid water as a function of temperature. However, The number of hydrogen bonds per water molecule extracted from the Compton profiles suggests a decrease of hydrogen bonds with rising temperatures for all studied samples, the differences between water and the solutions are weak. Nevertheless, the data indicate a reduced bond weakening with rising TMAO concentration up to 5M of 7.2% compared to 8 % for pure water. In contrast, the addition of GdnHCl appears to behave differently for concentrations up to 3.1 M with a weaker impact on the temperature response of the hydrogen bond structure.

Core-level spectra of liquids can be difficult to interpret due to the presence of a range of local environments. We present computational methods for investigating core-level spectra based on the idea that both local structural parameters and the X-ray spectra behave as functions of the local atomic configuration around the absorbing site. We identify correlations between structural parameters and spectral intensities in defined regions of interest, using the oxygen K-edge excitation spectrum of liquid water as a test case. Our results show that this kind of analysis can find the main structure-spectral relationships of ice, liquid water, and supercritical water., Comment: 10 Pages, 8 Figures

We report a study on the temperature dependence of the charge-neutral crystal field (dd) excitations in cupric oxide, using nonresonant inelastic x-ray scattering (IXS) spectroscopy. Thanks to a very high energy resolution (60 meV), we observe thermal effects on the dd excitation spectrum fine structure between temperatures of 10-320 K. With an increasing temperature, the spectra broaden considerably. We assign the temperature dependence of the dd excitations to the relatively large electron-phonon coupling., Comment: 5 pages, 2 figures