Department of Chemistry, The University of Chicago, 5735 S. Ellis Avenue, Chicago60637, Illinois, United States.
Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche No. 7019, Université de Lorraine, B.P. 70239, Vandœuvre-lès-Nancy cedex54506, France.
J Phys Chem Lett. 2022 Oct 13;13(40):9263-9271. doi: 10.1021/acs.jpclett.2c02529. Epub 2022 Sep 29.
The treatment of slow and rare transitions in the simulation of complex systems poses a great computational challenge. A powerful approach to tackle this challenge is the string method, which represents the transition path as a one-dimensional curve in a multidimensional space of collective variables. Commonly used strategies for pathway optimization include aligning the tangent of the string to the local mean force or to the mean drift determined from swarms of short trajectories. Here, a novel strategy is proposed, allowing the string to be optimized based on a variational principle involving the unidirectional reactive flux expressed in terms of the time-correlation function of the committor. The method is illustrated with model systems and then probed with the alanine dipeptide and a coarse-grained model of the barstar-barnase protein complex. Successive iterations variationally refine the string toward an optimal transition pathway following the gradient of the committor between two metastable states.
在模拟复杂系统时，处理缓慢且罕见的转变是一个巨大的计算挑战。一种解决这一挑战的强大方法是字符串方法，它将转变路径表示为在多个集体变量空间中的一维曲线。常用于路径优化的策略包括将字符串的切线与局部平均力对齐，或者与从短轨迹群确定的平均漂移对齐。在这里，提出了一种新的策略，允许根据涉及用位敏函数的时间相关函数表示的单向反应通量的变分原理来优化字符串。该方法通过模型系统进行了说明，然后使用丙氨酸二肽和枯草杆菌蛋白酶-枯草杆菌蛋白酶复合物的粗粒模型进行了探测。连续迭代根据两个亚稳态之间的位敏函数的梯度，变分地细化字符串，以找到最佳的转变路径。
