Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling, provided that information between the coarse and fine representations of molecules needs to be properly exchanged: One is to construct coarse grained (CG) models by passing information from the fine to coarse levels; the other is to restore finer molecular details given CG configurations. Although these two problems are commonly addressed independently, in this work, we present a theory connecting them, and develop a methodology called Cycle Coarse Graining (CCG) to solve both problems in a unified manner. In CCG, reconstruction can be achieved via a tractable optimization process, leading to a general method to retrieve fine details from CG simulations, which in turn, delivers a new solution to the CG problem, yielding an efficient way to calculate free energies in a rare-event-free manner. CCG thus provides a systematic way for multiscale molecular modeling, where the finer details of CG simulations can be efficiently retrieved, and the CG models can be improved consistently.

翻译：多尺度分子建模广泛应用于大时空尺度分子性质的科学研究。在多尺度建模中，由于需要在分子的粗粒化与精细表示之间进行有效信息交换，通常面临两个具体挑战：一是通过从精细层级向粗粒化层级传递信息来构建粗粒化模型；二是在给定粗粒化构型条件下恢复更精细的分子细节。尽管这两个问题通常被独立处理，但本文提出了连接二者的理论框架，并发展了一种名为循环粗粒化的方法论，以统一方式解决这两个问题。在CCG中，重建可通过可处理的优化过程实现，从而形成从粗粒化模拟中恢复精细细节的通用方法，这反过来为粗粒化问题提供了新方案，并实现了无稀有事件限制的自由能高效计算方法。因此，CCG为多尺度分子建模提供了系统化途径，既能高效恢复粗粒化模拟的精细细节，又能持续改进粗粒化模型。