力场(虚构)
分子动力学
粒度
灵活性(工程)
计算机科学
代表(政治)
Atom(片上系统)
领域(数学)
平均力势
算法
统计物理学
物理
人工智能
化学
计算化学
数学
并行计算
操作系统
统计
政治
政治学
纯数学
法学
作者
Andreas Krämer,Aleksander E. P. Durumeric,Nicholas Charron,Yaoyi Chen,Cecilia Clementi,Frank Noé
标识
DOI:10.1021/acs.jpclett.3c00444
摘要
Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning bottom-up CG force fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force field on average. We show that there is flexibility in how to map all-atom forces to the CG representation and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins chignolin and tryptophan cage and published as open-source code.
科研通智能强力驱动
Strongly Powered by AbleSci AI