分子模拟中不确定度和采样质量量化的最佳实践[第v1.0条]。

Alan Grossfield, Paul N Patrone, Daniel R Roe, Andrew J Schultz, Daniel W Siderius, Daniel M Zuckerman
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引用次数: 93

摘要

在分子模拟中,不确定度和采样质量的定量评估至关重要。许多感兴趣的系统是高度复杂的,通常处于当前计算能力的边缘。因此,建模人员必须分析和传达统计不确定性,以便模拟数据的“消费者”了解其重要性和局限性。本文介绍了适用于分子动力学和(单马尔可夫链)蒙特卡罗等传统模拟方法生成的轨迹数据的关键分析。它还为分析一些“增强型”采样方法提供了指导。我们不讨论由于所选模型或力场的不准确而产生的系统误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].

Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].

Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].

Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].

The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that "consumers" of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.

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