在非平衡粗粒模拟中解决纠的线性聚合物的动态特性,使用先验缩放因子
Yihan Nie1, Zhuoqun Zheng2, Chengkai Li3
1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Nanoscale
|March 18, 2024
概括
本研究引入了一种使用先验缩放因子的新方法,以改进粗粒度 (CG) 聚合物模拟. 这种方法提高了预测诸如粘度和模量等动态性质的准确性,克服了传统方法的局限性.
科学领域:
- 计算材料科学科学 计算材料科学
- 聚合物物理 聚合物物理
- 分子动力学模拟模型
背景情况:
- 聚合物的全原子模拟在计算上很昂贵,特别是用于预测受分子重量影响的特性.
- 传统的自下而上粗粒度 (CG) 模拟降低了成本,但往往预测不准确的动态特性 (例如,更快的扩散,更低的粘度/模块).
- 现有的纠正CG动态的方法 (a posteriori缩放) 缺乏基本的物理,并且具有较差的可转移性.
研究的目的:
- 开发一种准确且可转移的方法,使用粗粒度模拟来预测聚合物动态性质.
- 解决传统CG模拟在捕捉精确材料属性的局限性.
- 引入基于物理的方法来提高CG模型的可靠性.
主要方法:
- 根据自由度的损失计算出先验缩放因子.
- 在代的博尔兹曼逆转框架中实现了这些缩放因子.
- 通过在不同温度和负载率上使用三个CG级别验证了对聚烯酸的方法.
主要成果:
- 在CG模拟中,先验缩放因子准确地重现了动态特性,例如热容量,模和粘度.
- 该方法在预测聚合物的结构分布方面保持了准确性.
- 证明了该方法的可转移性,将其与-补偿和散射粒子动态恒温器进行比较.
结论:
- 提议的先验缩放方法显著提高了CG聚合物模拟中的动态性质预测的准确性和可转移性.
- 这种方法为CG模拟中的快速动态问题提供了一个基本的,基于物理的解决方案.
- 为开发定制的CG恒温器和重建具有增强预测能力的多物理CG模型铺平了道路.
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