{"ID":2869189,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16266","arxiv_id":"2509.16266","title":"Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction","abstract":"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.","short_abstract":"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 mo...","url_abs":"https://arxiv.org/abs/2509.16266","url_pdf":"https://arxiv.org/pdf/2509.16266v2","authors":"[\"Julian Konrad\",\"Janina Mittelhaus\",\"David M. Wilkins\",\"Bodo Fiedler\",\"Robert Meißner\"]","published":"2025-09-18T07:27:16Z","proceeding":"physics.chem-ph","tasks":"[\"physics.chem-ph\",\"cond-mat.mtrl-sci\",\"cs.LG\"]","methods":"[]","has_code":false}