Towards Structural Reconstruction from X-Ray Spectra

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

Temperature and pressure induced changes in the local atomic and electronic structure of complex materials

Changes in the local atomic and electronic structure of complex materials under high tempera- ture and high pressure conditions are explored by means of x-ray absorption spectroscopy and x-ray Raman scattering. Three consecutive studies on the formation of well-defined germanium (Ge) nanocrystals in oxide matrices are presented. The temperature induced phase separation and Ge nanocrystal formation in amorphous GeO is inverstigated using x-ray absorption spectroscopy and x-ray diffraction. Size control is achieved via a multilayer approach. Ge oxide free Ge nanocrystals in silicate matrices from ternary multilayers are reported on. The complex local environment of the Ba atoms and interactions between Ba guest and Si host atoms of Ba intercalated Si clathrates are studied using x-ray Raman scattering. The Ba giant resonance is investigated for several complex Ba intercalated Si clathrates and compounds and is shown to be dependent on the local environment of the Ba atoms. The nature of the peculiar pressure induced phase transitions in the clathrate Ba_8 Si_46 is investigated by measurements of the Ba multiplet features in the vicinity of the Ba N_4,5 absorption edge. First direct proof of the electronic topological nature of these isostructural phase transitions is presented. An x-ray Raman scattering study of water under high pressure and high temperature con- ditions offers new insight on the local atomic and electronic structure of water under such extreme conditions. In corroboration with molecular dynamic simulations, the results suggest supercritical water to consist of a highly disordered and disrupted but homogeneous hydrogen bond network. First measurements on geologically relevant silicate melts show the feasibility of such studies and implications for future experiments are given

Neural networks in interpretation of electronic core-level spectra

We explore the applicability of artificial intelligence for molecular structure - core-level spectrum interpretation. We focus on the electronic Hamiltonian using the H$_2$O 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 of the spectrum improves prediction. However, the accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space

Influence of TMAO and urea on the structure of water studied by inelastic X-ray scattering

We present a study on the influence of the naturally occurring organic osmolytes tri-methylamine N-oxide (TMAO) and urea on the bulk structure of water using X-ray Raman scattering spectroscopy. Addition of TMAO is known to stabilize proteins in otherwise destabilizing aqueous urea solutions. The experimental X-ray Raman scattering spectra change systematically with increasing solute concentration revealing different effects on the structure of water due to the presence of the two osmolytes. Although these effects are distinct for both molecular species, they have mutually compensating influences on the spectra of the ternary water-TMAO-urea mixtures. This compensation effect seen in the spectra vanishes only at the highest studied ternary concentration of 4 M: 4 M (TMAO : urea). Our experiment shows that the hydrogen-bonding structure of water remains rather intact in the presence of the aforementioned osmolytes if both of them are present.Peer reviewe

Temperature dependence of the hydrogen bond network in Trimethylamine N-oxide and guanidine hydrochloride - water solutions

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

Machine learning in interpretation of electronic core-level spectra

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.</p

Emulator-based decomposition for structural sensitivity of core-level spectra

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 behaviour 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.</p

Intramolecular structure and energetics in supercooled water down to 255 K

We studied the structure and energetics of supercooled water by means of X-ray Raman and Compton scattering. Under supercooled conditions down to 255 K, the oxygen K-edge measured by X-ray Raman scattering suggests an increase of tetrahedral order similar to the conventional temperature effect observed in non-supercooled water. Compton profile differences indicate contributions beyond the theoretically predicted temperature effect and provide a deeper insight into local structural changes. These contributions suggest a decrease of the electron mean kinetic energy by 3.3 +/- 0.7 kJ (mol K)(-1) that cannot be modeled within established water models. Our surprising results emphasize the need for water models that capture in detail the intramolecular structural changes and quantum effects to explain this complex liquid.Peer reviewe