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.

翻译：金属合金的氢捕获行为通常采用热脱附谱（TDS）进行表征。然而，作为一种间接方法，从中提取关键参数（陷阱结合能与陷阱密度）仍面临重大挑战。为克服这些局限，本研究提出一种基于机器学习的方案，用于从TDS谱中识别参数。研究开发了一个多神经网络模型，该模型完全基于合成数据训练，可直接根据实验数据预测捕获参数。该模型包含两个通过反向传播训练的多层全连接前馈神经网络：第一个网络（分类模型）用于预测不同陷阱类型的数量；第二个网络（回归模型）随后预测相应的陷阱密度与结合能。通过优化神经网络架构、超参数及数据预处理流程，最小化了训练数据需求量。将所提模型应用于三种不同成分的回火马氏体钢时，其展现出强大的预测能力。相关代码已开源提供。