Fu Haohao, Zhang Hong, Chen Haochuan, Shao Xueguang, Chipot Christophe, Cai Wensheng
Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition , Nankai University , Tianjin 300071 , China.
Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) , Tianjin 300071 , China.
J Phys Chem Lett. 2018 Aug 16;9(16):4738-4745. doi: 10.1021/acs.jpclett.8b01994. Epub 2018 Aug 7.
A robust importance-sampling algorithm for mapping free-energy surfaces over geometrical variables, coined meta-eABF, is introduced. This algorithm shaves the free-energy barriers and floods valleys by incorporating a history-dependent potential term in the extended adaptive biasing force (eABF) framework. Numerical applications on both toy models and nontrivial examples indicate that meta-eABF explores the free-energy surface significantly faster than either eABF or metadynamics (MtD) alone, without the need to stratify the reaction pathway. In some favorable cases, meta-eABF can be as much as five times faster than other importance-sampling algorithms. Many of the shortcomings inherent to eABF and MtD, like kinetic trapping in regions of configurational space already adequately sampled, the requirement of prior knowledge of the free-energy landscape to set up the simulation, are readily eliminated in meta-eABF. Meta-eABF, therefore, represents an appealing solution for a broad range of applications, especially when both eABF and MtD fail to achieve the desired result.
我们介绍了一种强大的重要性采样算法，用于在几何变量上绘制自由能表面，称为元扩展自适应偏置力（meta-eABF）。该算法通过在扩展自适应偏置力（eABF）框架中纳入一个依赖历史的势项，消除了自由能障碍并填充了低谷。在玩具模型和实际例子上的数值应用表明，meta-eABF比单独的eABF或元动力学（MtD）能更快地探索自由能表面，而无需对反应路径进行分层。在一些有利的情况下，meta-eABF的速度比其他重要性采样算法快五倍之多。eABF和MtD固有的许多缺点，如在已充分采样的构型空间区域中的动力学捕获、设置模拟所需的自由能景观先验知识等，在meta-eABF中都能轻易消除。因此，meta-eABF为广泛的应用提供了一个有吸引力的解决方案，特别是当eABF和MtD都无法达到预期结果时。
