Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909803814584320 |
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| author | Konrad, Julian Mittelhaus, Janina Wilkins, David M. Fiedler, Bodo Meißner, Robert |
| author_facet | Konrad, Julian Mittelhaus, Janina Wilkins, David M. Fiedler, Bodo Meißner, Robert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction Konrad, Julian Mittelhaus, Janina Wilkins, David M. Fiedler, Bodo Meißner, Robert Chemical Physics Materials Science Machine Learning 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. |
| title | Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction |
| topic | Chemical Physics Materials Science Machine Learning |
| url | https://arxiv.org/abs/2509.16266 |