Framework for Inverse Mapping Chemistry-Agnostic Coarse-Grained Simulation Models into Chemistry-Specific Models
Framework for Inverse Mapping Chemistry-Agnostic Coarse-Grained Simulation Models into Chemistry-Specific Models
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DOI：
10.1021/acs.jcim.9b00232
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发表时间：
2019-11
影响因子：
5.6
通讯作者：
C. Nowak;M. Misra;F. Escobedo
中科院分区：
文献类型：
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作者：
C. Nowak;M. Misra;F. Escobedo
粗粒度(CG)模型使分子模拟能够获得足够大的时间和长度尺度，以阐明宏观尺度性质和微观尺度分子相互作用之间的关系。然而，一个未解决的逆设计问题涉及到通用CG模型所代表的最佳化学特定(CS)分子的识别。这里通过引入新的工具来解决这一问题，这些工具用于根据代表性的优化标准自动生成和精炼CS-分子候选到CG模型的约束的映射。使用这些工具，对于候选基团中的每个CS-分子，找到该分子到CG模型的最佳映射，并通过目标函数来评估它们的适合性，该目标函数旨在强调匹配CG模型的关键属性。我们将这一方法应用于从小溶剂分子到嵌段共聚体系的一系列CG模型，以显示其找到最佳候选者的能力，并揭示一些CG模型的潜在长度尺度。对于CG模型的身份是先验已知的情况，该方法识别正确的AA化学。对于身份未知并且提供了候选者池的情况，该方法选择与物理直觉很好地一致的化学。最好的候选化学也被发现对CG模型的变化很敏感。
Coarse-grained (CG) models have allowed molecular simulations to access large enough time and length scales to elucidate relationships between macroscale properties and microscale molecular interactions. However, an unaddressed inverse-design problem concerns the identification of an optimal chemistry-specific (CS) molecule that the generic CG model represents. This has been addressed here by introducing new tools for automatically generating and refining the mapping of CS-molecule candidates to the constraints of a CG model, based on representative optimization criteria. With these tools, for each CS-molecule from a candidate group, the best mapping of that molecule onto the CG model is found and their fit assessed by an objective function designed to emphasize matching key properties of the CG model. We employ this methodology to a range of CG models from small solvent molecules up to block copolymer systems to show its ability to find optimal candidates, and to uncover the underlying length scale of some of the CG models. For instances where the identity of the CG model is known a priori, the methodology identifies the correct AA chemistry. For instances where the identity is unknown and a pool of candidates is provided, the method selects a chemistry that aligns well with physical intuition. The best candidate chemistry is also found to be sensitive to changes to the CG model.