An Efficient Descriptor for Tubular LDPE Melt Strength via Physics-Guided Learning and Diffusion-Based Inverse Design.

Authors

Yifei Wang, Guogang Liu, Lin Liu, Yue Yu, Peiqian Yu, Linge Ma, Linlin Wei, Zhe Zhou, Linfeng Chen, Delin Yuan, Rongjuan Cong

Published in

Polymers. Volume 18. Issue 19. Oct 04, 2026. Epub Oct 04, 2026.

Abstract

Melt strength (MS) is a key parameter governing the processability and application of low-density polyethylene (LDPE), dictated by a complex interplay of molecular weight distribution (MWD), long-chain branching (LCB), and high-molecular-weight content-features poorly captured by conventional metrics like weight average molecular weight (Mw). This paper addresses the challenge by developing a physics-guided machine learning framework that constructs a list of molecular descriptors directly from full-spectrum MWD data using modern multi-detector high-temperature gel permeation chromatography (MD-GPC), explicitly aligned with the polymer entanglement fundamentals underlying melt strength. The descriptor enables accurate prediction and interpretable disentanglement of topological contributions. The paper further introduced a diffusion-based generative model for inverse design: given a target melt strength, the model proposed candidate molecular architectures and validated them through internal consistency with the learned structure-property relationships, offering a rational and data-driven strategy for the precise engineering of LDPE molecular structures, thus providing considerable flexibility for downstream engineering decisions. Practitioners may further refine or screen these candidates in light of real-world factors-including polymerization processing constraints, cost considerations for resin selection and blending, and manufacturing operation windows-thereby tailoring LDPE molecular structures/products to specific application requirements with high precision.

PMID:
42885050
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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