Coarse-grained (CG) molecular models greatly reduce the computational cost of simulations allowing for longer and larger simulations, but come with an artificially increased acceleration of the dynamics when compared to the parent atomistic (AA) simulation. This impedes their use for the quantitative study of dynamical properties. During coarse-graining, grouping several atoms into one CG bead not only reduces the number of degrees of freedom but also reduces the roughness on the molecular surfaces, leading to the acceleration of dynamics. The RoughMob approach [M. K. Meinel and F. Müller-Plathe, J. Phys. Chem. B 126(20), 3737–3747 (2022)] quantifies this change in geometry and correlates it to the acceleration by making use of four so-called roughness volumes. This method was developed using simple one-bead CG models of a set of hydrocarbon liquids. Potentials for pure components are derived by the structure-based iterative Boltzmann inversion. In this paper, we find that, for binary mixtures of simple hydrocarbons, it is sufficient to use simple averaging rules to calculate the roughness volumes in mixtures from the roughness volumes of pure components and add a correction term quadratic in the concentration without the need to perform any calculation on AA or CG trajectories of the mixtures themselves. The acceleration factors of binary diffusion coefficients and both self-diffusion coefficients show a large dependence on the overall acceleration of the system and can be predicted a priori without the need for any AA simulations within a percentage error margin, which is comparable to routine measurement accuracies. Only if a qualitatively accurate description of the concentration dependence of the binary diffusion coefficient is desired, very few additional simulations of the pure components and the equimolar mixture are required.

中文翻译：

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使用粗糙度体积和简单平均规则预测粗粒化二元烃混合物的人工动态加速

粗粒度（CG）分子模型大大降低了模拟的计算成本，允许更长和更大的模拟，但与母体原子（AA）模拟相比，会人为地增加动力学加速度。这阻碍了它们用于动力学特性的定量研究。在粗粒化过程中，将多个原子聚集成一个CG珠不仅减少了自由度，而且还降低了分子表面的粗糙度，从而加速了动力学。 RoughMob 方法 [MK Meinel 和 F. Müller-Plathe, J. Phys.化学。 B 126(20), 3737–3747 (2022)] 量化了这种几何形状的变化，并通过使用四个所谓的粗糙度体积将其与加速度相关联。该方法是使用一组碳氢化合物液体的简单单珠 CG 模型开发的。纯组分的势是通过基于结构的迭代玻尔兹曼反演得出的。在本文中，我们发现，对于简单碳氢化合物的二元混合物，使用简单的平均规则从纯组分的粗糙度体积计算混合物中的粗糙度体积并添加浓度的二次校正项就足够了，而不需要对混合物本身的 AA 或 CG 轨迹进行任何计算。二元扩散系数和自扩散系数的加速因子显示出对系统整体加速度的很大依赖性，并且可以先验预测，无需在百分比误差范围内进行任何 AA 模拟，这与常规测量精度相当。仅当需要对二元扩散系数的浓度依赖性进行定性准确描述时，才需要对纯组分和等摩尔混合物进行很少的额外模拟。