复杂分子体系采样的基于口径的序参量谱隙优化（SGOOP）
Caliber based spectral gap optimization of order parameters (SGOOP) for sampling complex molecular systems
摘要：
在现代多体系统的模拟中，大部分计算复杂性转移到识别缓慢变化的分子序参量，称为集体变量（CV）或反应坐标。大量的增强采样方法是基于这些低维序参量的识别和偏置，其波动在驱动感兴趣的罕见事件中是重要的。这里描述了一种新的算法，用于寻找最佳的低维集体变量，用于增强的采样偏置方法，如伞形采样，元自适应和相关方法，当有限的先验静态和动态信息是已知的系统，并指定了一个更大的候选CV集。该算法涉及估计这些候选CV的最佳组合，如通过被视为CV的函数的动态的谱间隙的最大路径熵估计所量化的。通过多个实际的例子，我们展示了这个后处理过程可以导致CV的优化和几个数量级的改进，通过元自洽计算的自由能的收敛，基本上给出了即使从不成功的元自洽运行中提取有用信息的能力。
英文摘要：
In modern day simulations of many-body systems much of the computational complexity is shifted to the identification of slowly changing molecular order parameters called collective variables (CV) or reaction coordinates. A vast array of enhanced sampling methods are based on the identification and biasing of these low-dimensional order parameters, whose fluctuations are important in driving rare events of interest. Here describe a new algorithm for finding optimal low-dimensional collective variables for use in enhanced sampling biasing methods like umbrella sampling, metadynamics and related methods, when limited prior static and dynamic information is known about the system, and a much larger set of candidate CVs is specified. The algorithm involves estimating the best combination of these candidate CVs, as quantified by a maximum path entropy estimate of the spectral gap for dynamics viewed as a function of that CV. Through multiple practical examples, we show how this post-processing procedure can lead to optimization of CV and several orders of magnitude improvement in the convergence of the free energy calculated through metadynamics, essentially giving the ability to extract useful information even from unsuccessful metadynamics runs.
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