A neural network machine-learning approach for characterising hydrogen trapping parameters from TDS experiments

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. • A Machine Learning (ML) framework is proposed to interpret TDS experiments. • A multi-Neural Network (NN) model is presented and trained with synthetic data. • The model infers trap characteristics (density, energies) from experimental TDS data. • Strong predictive abilities are demonstrated for three case studies on martensitic steels. • The code developed is freely provided at https://mechmat.web.ox.ac.uk/codes .