Towards structural reconstruction from X-ray spectra.
Phys Chem Chem Phys
; 25(9): 6707-6713, 2023 Mar 01.
Article
in En
| MEDLINE
| ID: mdl-36804587
ABSTRACT
We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO2 at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the Kß'' and Kß2 peaks in the spectrum from the Coulomb matrix descriptor with a training set of â¼ 104 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 GeO2. 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 Kß emission spectrum alone.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Phys Chem Chem Phys
Journal subject:
BIOFISICA
/
QUIMICA
Year:
2023
Document type:
Article
Affiliation country: