基于机器学习的可逆粗粒化方法用于多尺度分子模拟（英文版）

英文标题：Machine-Learned Invertible Coarse Graining for Multiscale Molecular Modeling中文摘要：使用周期粗粒化 (CCG) 方法，能够在统一的框架下构建基于结构的粗粒化 (CG) 模型，并能通过可行的优化过程恢复细微的分子细节，从而实现多尺度分子建模中 CG 模型的改进与精度提高。英文摘要：Multiscale molecular modeling is widely applied in scientific research ofmolecular properties over large time and length scales. Two specific challengesare commonly present in multiscale modeling, provided that information betweenthe coarse and fine representations of molecules needs to be properlyexchanged: One i