Invernizzi Michele, Parrinello Michele
Department of Physics, ETH Zurich, c/o Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland.
Facoltà di Informatica, Institute of Computational Science, National Center for Computational Design and Discovery of Novel Materials (MARVEL), Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland.
J Phys Chem Lett. 2020 Apr 2;11(7):2731-2736. doi: 10.1021/acs.jpclett.0c00497. Epub 2020 Mar 23.
Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is a function of a selected number of collective variables. We propose here a change in perspective that shifts the focus from the bias to the probability distribution reconstruction while retaining some of the key characteristics of metadynamics, such as flexible on-the-fly adjustments to the free energy estimate. The result is an enhanced sampling method that presents a drastic improvement in convergence speed, especially when dealing with suboptimal and/or multidimensional sets of collective variables. The method is especially robust and easy to use and in fact requires only a few simple parameters to be set, and it has a straightforward reweighting scheme to recover the statistics of the unbiased ensemble. Furthermore, it gives more control of the desired exploration of the phase space since the deposited bias is not allowed to grow indefinitely and it does not push the simulation to uninteresting high free energy regions. We demonstrate the performance of the method in a number of representative examples.
元动力学是一种非常流行的增强采样方法，基于对偏差势的实时构建，该偏差势是所选数量的集体变量的函数。我们在此提出一种视角的转变，即将重点从偏差转移到概率分布重建，同时保留元动力学的一些关键特征，例如对自由能估计的灵活实时调整。结果是一种增强采样方法，其收敛速度有显著提高，特别是在处理次优和/或多维集体变量集时。该方法特别稳健且易于使用，实际上只需要设置几个简单参数，并且有一个直接的重加权方案来恢复无偏差系综的统计信息。此外，由于沉积的偏差不允许无限增长，并且不会将模拟推向无趣的高自由能区域，因此它能更好地控制对相空间的期望探索。我们在一些代表性示例中展示了该方法的性能。
