Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction
This work enables non-destructive stress mapping and structural-health diagnostics for advanced materials like polymers, though it is incremental as it builds on existing spectroscopic and computational methods.
The researchers tackled the problem of predicting mechanical states in polymer networks by developing machine-learned force fields that accurately reproduce vibrational spectroscopic fingerprints under load, achieving quantum-level fidelity in epoxy thermosets and capturing experimentally observed redshifts in stretching modes.
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.