Department of Chemical Engineering, University of California, Santa Barbara, California 93106, United States.
Materials Research Laboratory, University of California, Santa Barbara, California 93106, United States.
ACS Macro Lett. 2021 May 18;10(5):576-583. doi: 10.1021/acsmacrolett.1c00013. Epub 2021 Apr 22.
Polymer formulations possessing mesostructures or phase coexistence are challenging to simulate using atomistic particle-explicit approaches due to the disparate time and length scales, while the predictive capability of field-based simulations is hampered by the need to specify interactions at a coarser scale (e.g., χ-parameters). To overcome the weaknesses of both, we introduce a bottom-up coarse-graining methodology that leverages all-atom molecular dynamics to molecularly inform coarser field-theoretic models. Specifically, we use relative-entropy coarse-graining to parametrize particle models that are directly and analytically transformable into statistical field theories. We demonstrate the predictive capability of this approach by reproducing experimental aqueous poly(ethylene oxide) (PEO) cloud-point curves with no parameters fit to experimental data. This synergistic approach to multiscale polymer simulations opens the door to de novo exploration of phase behavior across a wide variety of polymer solutions and melt formulations.
聚合物制剂具有介观结构或相共存,由于时间和长度尺度的不同,使用原子粒子显式方法进行模拟具有挑战性,而基于场的模拟的预测能力受到需要在更粗糙的尺度上指定相互作用的限制(例如,χ 参数)。为了克服两者的弱点,我们引入了一种自下而上的粗粒化方法,该方法利用全原子分子动力学为更粗糙的场论模型提供信息。具体来说,我们使用相对熵粗粒化来参数化可以直接和分析地转换为统计场论的粒子模型。我们通过重现实验性水相聚(乙)氧化(PEO)浊点曲线来证明该方法的预测能力,而无需拟合实验数据的参数。这种多尺度聚合物模拟的协同方法为在各种聚合物溶液和熔体制剂中探索相行为开辟了新的途径。