| Autor: |
Penfold, T. J., Rankine, C. D. |
| Předmět: |
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| Zdroj: |
Molecular Physics; Apr2023, Vol. 121 Issue 7/8, p1-13, 13p |
| Abstrakt: |
In this Article, we extend our XANESNET deep neural network (DNN) to predict the lineshape of first-row transition metal K-edge valence-to-core X-ray emission (VtC-XES) spectra. We demonstrate that – despite the strong sensitivity of VtC-XES to the electronic structure of the system under study – the DNN can reproduce the main spectral features from only the local coordination geometry of the transition metal complexes when encoded as a feature vector of weighted atom-centred symmetry functions (wACSF). We subsequently implement and evaluate three methods for assessing uncertainty in the predictions made by the VtC-DNN: deep ensembles, Monte-Carlo dropout, and bootstrap resampling. We show that bootstrap resampling provides the best performance when evaluated on 'held-out' testing data, and also demonstrates a strong correlation between the uncertainty it predicts and the error occurring between the target and predicted VtC-XES spectra. Finally, we demonstrate practical performance by application to unseen transition metal complexes across the entire first-row (Ti–Zn). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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