用光谱指纹预测聚合物受力状态，实现无损检测新路径
Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction
- 用机器学习力场模拟聚合物受力时的振动光谱响应
- 准确复现了实验中对位苯环伸缩模红移现象
- 适合材料力学诊断与结构健康监测研究者参考
聚合物网络在受力下的振动响应可灵敏反映分子形变，为无损诊断提供途径。本文展示，机器学习力场在真实环氧热固性聚合物中实现了量子级精度的光谱指纹再现。利用MACE-OFF23分子动力学，我们成功捕捉到拉伸载荷下对位苯环伸缩模式的实验观测红移，而谐波OPLS-AA模型未能实现。这些频移与分子拉伸和取向相关，符合Badger规则，直接将振动特征与局部应力关联。为建模红外强度，我们在代表性环氧片段上训练了对称性自适应偶极矩模型，验证了应变响应。该方法实现化学精确且计算高效地预测应变依赖的振动光谱。结果确立了振动指纹作为聚合物网络机械状态的预测标志，为先进材料中的应力映射与结构健康诊断提供了新策略。
原文摘要 · Abstract (English)
The vibrational response of polymer networks under load provides a sensitive probe of molecular deformation and a route to non-destructive diagnostics. Here we show that machine-learned force fields reproduce these spectroscopic fingerprints with quantum-level fidelity in realistic epoxy thermosets. Using MACE-OFF23 molecular dynamics, we capture the experimentally observed redshifts of para-phenylene stretching modes under tensile load, in contrast to the harmonic OPLS-AA model. These shifts correlate with molecular elongation and alignment, consistent with Badger's rule, directly linking vibrational features to local stress. To capture IR intensities, we trained a symmetry-adapted dipole moment model on representative epoxy fragments, enabling validation of strain responses. Together, these approaches provide chemically accurate and computationally accessible predictions of strain-dependent vibrational spectra. Our results establish vibrational fingerprints as predictive markers of mechanical state in polymer networks, pointing to new strategies for stress mapping and structural-health diagnostics in advanced materials.
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