用神经网络从氢脱附数据中自动提取陷阱参数，提升材料研究效率。
A neural network machine-learning approach for characterising hydrogen trapping parameters from TDS experiments
- 构建双神经网络模型，先分类后回归，直接从实验TDS谱图预测陷阱类型
- 在三种不同成分的回火马氏体钢上验证，参数预测准确率高
- 仅需少量合成数据训练，适合缺乏先验知识的材料开发场景
金属合金中的氢陷阱行为通常通过热脱附光谱（TDS）表征。然而，作为间接方法，提取关键参数（陷阱结合能与密度）仍具挑战性。本文提出一种基于机器学习的参数识别方案，利用合成数据训练多层神经网络模型，直接从实验TDS谱图预测陷阱参数。模型包含两个全连接前馈神经网络：第一网络（分类模型）预测不同陷阱类型的数量；第二网络（回归模型）则预测对应的陷阱密度与结合能。通过优化网络结构、超参数及数据预处理，显著减少对训练数据量的需求。该方法在三种不同成分的回火马氏体钢上表现出优异的预测性能。相关代码已开源提供。
原文摘要 · Abstract (English)
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.
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