Towards Structural Reconstruction from X-Ray Spectra

Abstract

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${\beta }^{\prime \prime }$ 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.

References in corpus (10)

• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Bethe-Salpeter Equation Calculations of Core Excitation Spectra
• Unified Representation of Molecules and Crystals for Machine Learning
• Efficient implementation of core-excitation Bethe Salpeter equation calculations
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Machine-Learning X-ray Absorption Spectra to Quantitative Accuracy
• Classification of Local Chemical Environments from X-ray Absorption Spectra using Supervised Machine Learning
• Disentangling Structural Information From Core-level Excitation Spectra
• Emulator-based Decomposition for Structural Sensitivity of Core-level Spectra
• Machine learning in interpretation of electronic core-level spectra

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