发表机构
University of North Texas（北德克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用AI加速的从头算自下而上框架，结合机器学习力场模拟pDXL活性聚合，预测其无定形微结构，并解析衍射信号为单体与聚合物两种模式，为实验验证奠定基础。
AI 中文摘要
聚(1,3-二氧戊环)（pDXL）是一种可化学回收的聚醚，可通过活性阳离子开环聚合合成，并精确控制链长至超高分子量（UHMW）区间，在该区间内其力学性能得到增强。然而，与大多数聚合物一样，它具有无定形凝聚相结构，仅靠衍射测量无法解析其背后的原子级微观结构。在本工作中，我们通过应用最近开发的AI加速的从头算自下而上聚合物结构预测（AI$^{2}$-BPSP）框架来预测pDXL微结构的系综，该框架在范德华修正的杂化密度泛函理论内训练的机器学习力场下，模拟实验合成条件下的pDXL活性聚合。将预测的X射线和中子衍射信号在生长链与周围单体之间进行划分，可将其主要特征分解为两种相互抵消的模式：在$q \approx 1.5$ Å$^{-1}$附近的单体贡献，随着单体消耗而衰减；以及在$q \approx 1.6$ Å$^{-1}$附近的聚合物贡献，该贡献在低聚物中不存在，并随链长增长而增加。这项工作为这些具有统计显著性、基于第一性原理且分辨链长的衍射预测与实验的直接精度验证铺平了道路。
英文摘要
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 $q \approx 1.5$ Å$^{-1}$ that decays as monomer is consumed, and a polymer contribution near $q \approx 1.6$ 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.
Comments10 pages, 7 figures. Supplementary material is available with the published article
