Zhou Hongyu, Tao Peng
Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University , Dallas, Texas 75275, United States of America.
J Chem Theory Comput. 2018 Jan 9;14(1):14-29. doi: 10.1021/acs.jctc.7b00606. Epub 2017 Dec 18.
The minimum energy pathway contains important information describing the transition between two states on a potential energy surface (PES). Chain-of-states methods were developed to efficiently calculate minimum energy pathways connecting two stable states. In the chain-of-states framework, a series of structures are generated and optimized to represent the minimum energy pathway connecting two states. However, multiple pathways may exist connecting two existing states and should be identified to obtain a full view of the transitions. Therefore, we developed an enhanced sampling method, named as the direct pathway dynamics sampling (DPDS) method, to facilitate exploration of a PES for multiple pathways connecting two stable states as well as addition minima and their associated transition pathways. In the DPDS method, molecular dynamics simulations are carried out on the targeting PES within a chain-of-states framework to directly sample the transition pathway space. The simulations of DPDS could be regulated by two parameters controlling distance among states along the pathway and smoothness of the pathway. One advantage of the chain-of-states framework is that no specific reaction coordinates are necessary to generate the reaction pathway, because such information is implicitly represented by the structures along the pathway. The chain-of-states setup in a DPDS method greatly enhances the sufficient sampling in high-energy space between two end states, such as transition states. By removing the constraint on the end states of the pathway, DPDS will also sample pathways connecting minima on a PES in addition to the end points of the starting pathway. This feature makes DPDS an ideal method to directly explore transition pathway space. Three examples demonstrate the efficiency of DPDS methods in sampling the high-energy area important for reactions on the PES.
最小能量路径包含描述势能面(PES)上两个状态之间转变的重要信息。态链方法被开发出来以有效地计算连接两个稳定状态的最小能量路径。在态链框架中,生成并优化一系列结构以表示连接两个状态的最小能量路径。然而,连接两个现有状态可能存在多条路径,应该识别这些路径以全面了解转变情况。因此,我们开发了一种增强采样方法,称为直接路径动力学采样(DPDS)方法,以促进对PES的探索,用于连接两个稳定状态的多条路径以及附加极小值及其相关的转变路径。在DPDS方法中,在态链框架内对目标PES进行分子动力学模拟,以直接采样转变路径空间。DPDS的模拟可以通过控制沿路径的状态间距离和路径平滑度的两个参数来调节。态链框架的一个优点是生成反应路径不需要特定的反应坐标,因为此类信息由沿路径的结构隐含表示。DPDS方法中的态链设置极大地增强了在两个终态(如过渡态)之间的高能空间中的充分采样。通过消除对路径终态的约束,DPDS除了起始路径的端点外,还将采样连接PES上极小值的路径。这一特性使DPDS成为直接探索转变路径空间的理想方法。三个例子展示了DPDS方法在采样对PES上反应重要的高能区域方面的效率。