A neural network machine-learning approach for characterising hydrogen trapping parameters from TDS experiments
File(s)
Published version
Author(s)
Marrani, N
Hageman, T
Martínez-Pañeda, E
Type
Journal Article
Abstract
The hydrogen trapping behaviour of metallic alloys is generally characterised using Thermal Desorption Spectroscopy (TDS). However, as an indirect method, extracting key parameters (trap binding energies and densities) remains a significant challenge. To address these limitations, this work introduces a machine learning-based scheme for parameter identification from TDS spectra. A multi-Neural Network (NN) model is developed and trained exclusively on synthetic data to predict trapping parameters directly from experimental data. The model comprises two multi-layer, fully connected, feed-forward NNs trained with backpropagation. The first network (classification model) predicts the number of distinct trap types. The second network (regression model) then predicts the corresponding trap densities and binding energies. The NN architectures, hyperparameters, and data pre-processing were optimised to minimise the amount of training data. The proposed model demonstrated strong predictive capabilities when applied to three tempered martensitic steels of different compositions. The code developed is freely provided.
Date Issued
2025-09-11
Date Acceptance
2025-08-04
Citation
International Journal of Hydrogen Energy, 2025, 167
ISSN
0360-3199
Publisher
Elsevier
Journal / Book Title
International Journal of Hydrogen Energy
Volume
167
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
150874
Date Publish Online
2025-08-20