Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures
Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures
复制标题
均质流体混合物局部密度辅助隐式溶剂化模型的可传递性
DOI：
10.1021/acs.jctc.8b01170
复制
发表时间：
2019
影响因子：
5.5
通讯作者：
van der Vegt, Nico F.
中科院分区：
文献类型：
--
作者：
Rosenberger, David;Sanyal, Tanmoy;Shell, M. Scott;van der Vegt, Nico F.
应用自下而上的粗粒（CG）模型来研究液体的平衡混合行为是相当具有挑战性的，因为这些模型可以显着影响的密度或浓度的状态选择在参数化。这种依赖性导致密度/浓度空间的低可转移性，并且是自下而上粗粒化的主要限制之一。最近提出的方法来解决这个缺点的范围从添加热力学约束，扩展合奏参数化，补充条款的系统的哈密顿量。为了研究自下而上CG模型的流体相平衡，局部密度势（LD）的应用似乎是一种有前途的方法，如Sanyal和Shell [T. Sanyal，M. S. Shell，J. Phys. Chem. B，2018，122，5678]。在这里，我们想进一步探索这种方法，并测试其建模的能力，其中包含的结构不均匀性仅在分子尺度上，即，甲醇和水的溶液的系统。我们发现，水-水LD势提高了隐式甲醇CG模型向高水浓度的可转移性。相反，甲醇-甲醇LD势不会显著提高隐水CG模型向高甲醇浓度的可转移性。出现这些差异是由于在高浓度的LD电位可以捕获的水中的合作相互作用的存在。此外，我们比较了两种不同的方法来推导我们的CG模型，即相对熵优化和逆蒙特卡罗方法，并正式证明了这两种方法的分析和数值假设下产生等效的结果。
The application of bottom-up coarse grained (CG) models to study the equilibrium mixing behavior of liquids is rather challenging, since these models can be significantly influenced by the density or the concentration of the state chosen during parametrization. This dependency leads to low transferability in density/concentration space and has been one of the major limitations in bottom-up coarse graining. Recent approaches proposed to tackle this shortcoming range from the addition of thermodynamic constraints, to an extended ensemble parametrization, to the addition of supplementary terms to the system’s Hamiltonian. To study fluid phase equilibria with bottom-up CG models, the application of local density (LD) potentials appears to be a promising approach, as shown in previous work by Sanyal and Shell [T. Sanyal, M. S. Shell,J. Phys. Chem. B,2018,122, 5678]. Here, we want to further explore this method and test its ability to model a system which contains structural inhomogeneities only on the molecular scale, namely, solutions of methanol and water. We find that a water–water LD potential improves the transferability of an implicit-methanol CG model toward high water concentration. Conversely, a methanol–methanol LD potential does not significantly improve the transferability of an implicit-water CG model toward high methanol concentration. These differences appear due to the presence of cooperative interactions in water at high concentrations that the LD potentials can capture. In addition, we compare two different approaches to derive our CG models, namely, relative entropy optimization and the Inverse Monte Carlo method, and formally demonstrate under which analytical and numerical assumptions these two methods yield equivalent results.