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Uncertainty quantification of spectral predictions using deep neural networks.
- Source :
- Chemical Communications; 6/11/2023, Vol. 59 Issue 46, p7100-7103, 4p
- Publication Year :
- 2023
-
Abstract
- We investigate the performance of uncertainty quantification methods, namely deep ensembles and bootstrap resampling, for deep neural network (DNN) predictions of transition metal K-edge X-ray absorption near-edge structure (XANES) spectra. Bootstrap resampling combined with our multi-layer perceptron (MLP) model provides an accurate assessment of uncertainty with >90% of all predicted spectral intensities falling within ±3σ of the true values for held-out data across the nine first-row transition metal K-edge XANES spectra. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13597345
- Volume :
- 59
- Issue :
- 46
- Database :
- Complementary Index
- Journal :
- Chemical Communications
- Publication Type :
- Academic Journal
- Accession number :
- 164129666
- Full Text :
- https://doi.org/10.1039/d3cc01988h