Uncertainty quantification in classical molecular dynamics.
Uncertainty quantification in classical molecular dynamics.
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DOI：
10.1098/rsta.2020.0082
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发表时间：
2021-05-17
期刊：
影响因子：
--
通讯作者：
Coveney PV
中科院分区：
文献类型：
--
作者：
Wan S;Sinclair RC;Coveney PV
分子动力学模拟是一种在原子尺度上理解复杂系统的广泛方法。它的应用范围从物理和化学到工程、生命和医学科学。在过去的十年中，这种方法已经开始从基于计算机的合理化实验观察的手段，为先进材料和药物发现等工业部门的一些实际应用产生明显可信的预测。然而，关于该方法的可重复性的关键方面并没有跟上科学界对其吸收的速度。在这里，我们提出了一个讨论的分子动力学模拟的不确定性量化，旨在赋予更好的误差估计，使其能够用于报告可操作的结果的方法。所采用的方法是一个标准的不确定性量化领域，即使用合奏方法，其中一个足够大的副本同时运行，从中可以提取可靠的统计数据。事实上，因为分子动力学本质上是混沌的，所以无论进行模拟的持续时间如何，都需要使用系综方法。我们讨论的方法，并说明它在一系列的应用，从材料科学配体-蛋白质结合自由能估计。这篇文章是“计算科学中的可靠性和再现性：在计算机中实现验证，验证和不确定性量化”主题的一部分。
Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand–protein binding free energy estimation. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico’.
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