Issue 46, 2023

Uncertainty quantification of spectral predictions using deep neural networks

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.

Graphical abstract: Uncertainty quantification of spectral predictions using deep neural networks

Supplementary files

Article information

Article type
Communication
Submitted
24 Apr 2023
Accepted
16 May 2023
First published
16 May 2023
This article is Open Access
Creative Commons BY license

Chem. Commun., 2023,59, 7100-7103

Uncertainty quantification of spectral predictions using deep neural networks

S. Verma, N. K. N. Aznan, K. Garside and T. J. Penfold, Chem. Commun., 2023, 59, 7100 DOI: 10.1039/D3CC01988H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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