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首页> 外文期刊>Macromolecular theory and simulations >Energy Renormalization for Coarse-Graining Polymers with Different Fragilities: Predictions from the Generalized Entropy Theory
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Energy Renormalization for Coarse-Graining Polymers with Different Fragilities: Predictions from the Generalized Entropy Theory

机译:具有不同脆性的粗晶体聚合物的能量重整:广义熵理论的预测

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摘要

Bottom-up prediction of physical performance of glass-forming (GF) polymers via coarse-grained (CG) modeling is challenging because these CG models normally experience significantly altered dynamics that strongly vary with temperature. Building upon the recently developed energy-renormalization (ER) coarse-graining method based on molecular dynamics simulations, generalized entropy theory (GET) is employed to theoretically investigate the influence of fundamental molecular parameters on CG modeling of polymers having different glass "fragilities" Taking a linear polymer melt as a model system within the GET framework, it is shown that the chain bending rigidity and cohesive interaction play critical roles in the glass formation of polymers and their CG analogs. To coarse-grain polymers having a higher fragility index, it requires greater magnitudes of ER factor epsilon(CG) to rescale the cohesive interaction strength under coarse-graining and thus recover the atomistic relaxation dynamics over a wide temperature range. The GET further predicts that a higher degree of coarse-graining generally requires greater magnitudes of epsilon(CG) due to the influence of loss of configuration entropy s(c) on the dynamics. GET analyses herein theoretically demonstrate the efficacy of the ER method toward building a multiscale temperature transferable modeling framework for GF polymers, and confirm the importance of preserving s(c) in CG modeling of dynamics of soft materials.
机译:通过粗粒(CG)建模的玻璃形成(GF)聚合物的物理性能的自下而上预测是具有挑战性的,因为这些CG模型通常经历显着改变温度的动态。基于分子动力学模拟的最近开发的能量 - 重形(ER)粗磨法,通过了广义熵理论(GET),从理论上探讨了基本分子参数对具有不同玻璃“脆性”的聚合物CG模拟的影响作为在框架内的型号系统的线性聚合物熔体,结果表明,链弯曲刚性和粘性相互作用在聚合物和其CG类似物的玻璃形成中起重要作用。对于具有较高脆性指数的粗晶粒聚合物,需要更大的ER因子ε(CG)以重新粗糙度下的粘性相互作用强度,从而在宽温度范围内恢复原子松弛动力学。进一步预测,由于在动态上的配置熵S(c)损失的影响,较高程度的粗粒子通常需要更大的ε(cg)幅度。在本文中进行分析理论上证明了ER方法对构建GF聚合物的多尺度温度可转移建模框架的功效,并确认了柔软材料动态的CG模型中保留了S(c)的重要性。

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