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

TL;DR

This paper presents a machine learning approach using neural networks trained on synthetic data to accurately determine hydrogen trapping parameters from TDS spectra, improving the analysis of metallic alloys.

Contribution

Introduces a neural network-based method for extracting hydrogen trapping parameters from TDS data, trained solely on synthetic data for improved accuracy and efficiency.

Findings

Accurately predicts trap types, densities, and energies from experimental TDS spectra.

Demonstrates strong predictive performance on steels of different compositions.

Provides open-source code for the developed neural network models.

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.