# Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction

arXiv: https://arxiv.org/abs/2509.16266 · HTML: https://mlsanity.com/paper/2509.16266/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials](https://mlsanity.com/paper/2101.03164/) (2021)
- [MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields](https://mlsanity.com/paper/2206.07697/) (2022)
- [Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics](https://mlsanity.com/paper/2204.05249/) (2022)
- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles](https://mlsanity.com/paper/2003.12437/) (2020)
- [Committee neural network potentials control generalization errors and enable active learning](https://mlsanity.com/paper/2006.01541/) (2020)
- [Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation](https://mlsanity.com/paper/2503.02678/) (2025)

## Abstract (arXiv)

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.