基于机器学习力场的高压自由基聚合过程中聚乙烯的第一性原理原子级结构与动力学
First-Principles Atomistic Structure and Dynamics of Polyethylene During High-Pressure Radical Polymerization via Machine Learning Force Fields
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中文总结 AI 辅助
该研究结合深度势机器学习力场与第一性原理DFT，探究高压自由基聚合下PE低聚物与聚合物的结构动力学，证实模型可推广至长链，为基于第一性原理的聚合物预测奠定基础。
中文摘要 AI 辅助
聚乙烯（PE）是最常用的合成聚合物之一。尽管PE的合成与加工方案已成熟，但原子级分辨率下微观结构（即每个原子的位置）的精确实验表征，目前大多仅局限于高结晶度体系。这一缺口常通过使用经验原子间势的计算机模拟来弥补，这类势采用近似但高效的原子间相互作用描述，以达到描述大分子所需的长度与时间尺度。这些经验势通常对体相或集体性质表现良好，但在复杂体系（如反应过程）的化学真实性方面面临挑战。本研究中，我们结合深度势（DP）机器学习力场的计算效率，以及由SeA高通量框架实现的第一性原理范德华（vdW）校正混合密度泛函理论（DFT）的化学真实性，解决这一挑战。采用该方法，我们研究了常见高压（超临界）自由基聚合条件下，乙烯溶剂中PE低聚物与聚合物的结构与动力学。我们发现，含自由基的PE低聚物的局部溶剂化环境在链长大于n≈6时趋于收敛，表明我们经低聚物训练的机器学习力场（MLFF）可推广至显著更长的聚合物。随后，我们通过表征单链结构与动力学的分子量标度关系，证实了这些模型对长PE链的可推广性，结果显示其呈现经典的良溶剂行为。我们的PE MLFF在宽热力学状态点范围与链长范围内，保持了全原子分辨率下的一致保真度与稳定性，为基于第一性原理的聚合物结构与性质预测铺平了道路。
英文摘要
Polyethylene (PE) is one of the most commonly used synthetic polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom) remains largely limited to highly crystalline systems. This gap is often addressed via computer simulations using empirical interatomic potentials, which use approximate but efficient descriptions of interatomic interactions to reach the length and time scales needed to describe macromolecules. These empirical potentials typically perform well for bulk and/or collective properties but face challenges with chemical realism for complex systems, e.g., during reactive processes. In this work, we address this challenge by combining the computational efficiency of a deep potential (DP) machine-learning force field and the chemical realism of first-principles van der Waals (vdW) corrected hybrid density functional theory (DFT) enabled by a SeA high-throughput framework. Using this approach, we study the structure and dynamics of PE oligomers and polymers in an ethylene solvent under common high-pressure (supercritical) radical polymerization conditions. We found that the local solvation environment of radical-containing PE oligomers converges for chain lengths greater than (n~6), suggesting extensibility of our oligomer-trained MLFF to significantly longer polymers. We then confirmed the extensibility of these models to long PE chains by characterizing the molecular weight scaling of single-chain structure and dynamics, which showed classic good solvent behavior. Our PE MLFF retained a consistent level of fidelity and stability across a wide range of thermodynamic state points and chain lengths, at full atomistic resolution, therefore paving the way towards first-principles-based polymer structure and property prediction.
发表机构
- University of North Texas（北得克萨斯大学）
- Lehigh University（理海大学）
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