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’.

分子动力学模拟现在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，该方法已经开始从一种基于计算机的使实验观察合理化的方法发展到为先进材料和药物发现等工业领域的许多实际应用提供明显可信的预测。然而，有关该方法的可重复性的关键方面并没有跟上其在科学界的采用速度。在这里，我们讨论了分子动力学模拟的不确定性量化，旨在赋予该方法更好的误差估计，使其能够用于报告可操作的结果。所采用的方法是不确定性量化领域的标准方法，即使用集成方法，其中同时运行足够多的副本，从中可以提取可靠的统计数据。事实上，由于分子动力学本质上是混沌的，因此使用系综方法的必要性是根本性的，并且无论执行模拟的持续时间如何，都保持不变。我们讨论该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说明它。

本文是主题“计算科学中的可靠性和再现性：在计算机中实现验证、确认和不确定性量化”的一部分。