Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK.
Institute for Informatics, Science Park 904, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
Philos Trans A Math Phys Eng Sci. 2021 May 17;379(2197):20200082. doi: 10.1098/rsta.2020.0082. Epub 2021 Mar 29.
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 '.
分子动力学模拟现在是一种广泛用于理解原子尺度复杂系统的方法。它的应用领域从物理和化学到工程、生命和医学科学。在过去的十年中，该方法已经开始从一种基于计算机的合理化实验观察的手段发展到对先进材料和药物发现等工业领域的许多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接受速度。在这里，我们提出了一种用于分子动力学模拟的不确定性量化的讨论，旨在为该方法赋予更好的误差估计，从而使其能够用于报告可操作的结果。采用的方法是不确定性量化领域中的一种标准方法，即使用集合方法，其中同时运行足够数量的副本，从中可以提取可靠的统计信息。事实上，由于分子动力学本质上是混沌的，因此需要使用集合方法，这是基本的，无论执行的模拟持续时间如何。我们讨论了这种方法，并在从材料科学到配体-蛋白质结合自由能估计的一系列应用中进行了说明。本文是“计算科学中的可靠性和可重复性：实施验证、验证和不确定性量化”主题问题的一部分。
