Bottom-Up Prediction of Amorphous Poly(1,3-dioxolane) using Ab Initio Reactive Machine-Learning Force Fields

Abstract

Poly(1,3-dioxolane) (pDXL) is a chemically recyclable polyether that can be synthesized by living cationic ring-opening polymerization with precise control of chain length into the ultra-high-molecular-weight (UHMW) regime, where it acquires enhanced mechanical properties. Like most polymers, however, it has amorphous condensed-phase structure, and diffraction measurements alone cannot resolve the atomistic microstructure that underlies them. In this work, we predict the ensemble of the pDXL microstructures by applying a recently developed AI-accelerated ab initio bottom-up polymer structure prediction (AI²-BPSP) framework that simulates the living polymerization of pDXL under experimental synthetic conditions with machine-learning force fields trained within van der Waals-corrected hybrid density functional theory. Partitioning the predicted X-ray and neutron diffraction signal between the growing chain and the surrounding monomer resolves its dominant feature into two counteracting modes: a monomer contribution near q ≈ 1.5 Å⁻¹ that decays as monomer is consumed, and a polymer contribution near q ≈ 1.6 Å⁻¹ that is absent in oligomers and grows with chain length. This work paves the way to direct accuracy validation of these statistically significant, first-principles, and chain-length resolved diffraction predictions against experiments.

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BibTeX RIS

BibTeX

@misc{song2026bottomup,
  title = {{Bottom-Up Prediction of Amorphous Poly(1,3-dioxolane) using Ab Initio Reactive Machine-Learning Force Fields}},
  author = {Yizhi Song and Bharatha K. Gunawardana and Kriti Alam and Teresa Shah and Deepa Ranabhat and Hsin-Yu Ko},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.09388},
  eprint = {2610.09388},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.09388},
}

RIS

TY  - UNPB
TI  - Bottom-Up Prediction of Amorphous Poly(1,3-dioxolane) using Ab Initio Reactive Machine-Learning Force Fields
AU  - Yizhi Song
AU  - Bharatha K. Gunawardana
AU  - Kriti Alam
AU  - Teresa Shah
AU  - Deepa Ranabhat
AU  - Hsin-Yu Ko
PY  - 2026
DA  - 2026/10/07
PB  - arXiv
DO  - 10.48550/arxiv.2610.09388
UR  - https://arxiv.org/abs/2610.09388
AB  - Poly(1,3-dioxolane) (pDXL) is a chemically recyclable polyether that can be synthesized by living cationic ring-opening polymerization with precise control of chain length into the ultra-high-molecular-weight (UHMW) regime, where it acquires enhanced mechanical properties. Like most polymers, however, it has amorphous condensed-phase structure, and diffraction measurements alone cannot resolve the atomistic microstructure that underlies them. In this work, we predict the ensemble of the pDXL microstructures by applying a recently developed AI-accelerated ab initio bottom-up polymer structure prediction (AI^2-BPSP) framework that simulates the living polymerization of pDXL under experimental synthetic conditions with machine-learning force fields trained within van der Waals-corrected hybrid density functional theory. Partitioning the predicted X-ray and neutron diffraction signal between the growing chain and the surrounding monomer resolves its dominant feature into two counteracting modes: a monomer contribution near q ≈ 1.5 Å^−1 that decays as monomer is consumed, and a polymer contribution near q ≈ 1.6 Å^−1 that is absent in oligomers and grows with chain length. This work paves the way to direct accuracy validation of these statistically significant, first-principles, and chain-length resolved diffraction predictions against experiments.
ER  -

Abstract

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