Metadynamics is an atomistic simulation technique that allows, within the same framework, acceleration of rare events and estimation of the free energy of complex molecular systems. It is based on iteratively ‘filling’ the potential energy of the system by a sum of Gaussians centred along the trajectory followed by a suitably chosen set of collective variables (CVs), thereby forcing the system to migrate from one minimum to the next. The power of metadynamics is demonstrated by the large number of extensions and variants that have been developed. The first scope of this Technical Review is to present a critical comparison of these variants, discussing their advantages and disadvantages. The effectiveness of metadynamics, and that of the numerous alternative methods, is strongly influenced by the choice of the CVs. If an important variable is neglected, the resulting estimate of the free energy is unreliable, and predicted transition mechanisms may be qualitatively wrong. The second scope of this Technical Review is to discuss how the CVs should be selected, how to verify whether the chosen CVs are sufficient or redundant, and how to iteratively improve the CVs using machine learning approaches.

元动力学是一种原子模拟技术，可以在相同的框架内加速罕见事件的发生并估计复杂分子系统的自由能。它基于以沿着轨迹为中心的高斯之和迭代地“填充”系统的势能，然后再选择一组合适的集合变量（CV），从而迫使系统从一个最小值迁移到另一个最小值。大量的扩展和变体已经证明了元动力学的力量。本技术审查的第一个范围是提出这些变体的关键比较，讨论它们的优缺点。元动力学的有效性以及众多替代方法的有效性在很大程度上取决于CV的选择。如果忽略了一个重要变量，则对自由能的最终估计将是不可靠的，并且预测的过渡机制可能在质上是错误的。本技术审查的第二个范围是讨论如何选择CV，如何验证所选的CV是足够还是多余以及如何使用机器学习方法迭代地改进CV。