Computational Science, Department of Chemistry and Applied Biosciences, Eidgenössiche Technische Hochschule Zurich, Università della Svizzera Italiana Campus, Via Giuseppe Buffi 13 C-6900 Lugano, Switzerland.
Proc Natl Acad Sci U S A. 2010 Oct 12;107(41):17509-14. doi: 10.1073/pnas.1011511107. Epub 2010 Sep 27.
A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.
提出了一种新的加速动力学的自学习算法,即侦察元动力学,它能够处理大量的集体坐标。通过根据一维、局部有效的集体坐标的拼凑构建偏置势来实现动力学的加速。这些集体坐标是从轨迹分析中获得的,以便它们适应模拟过程中遇到的任何新特征。我们展示了如何使用这种方法来增强真实化学系统中的采样,引用了来自团簇物理学和生物科学的例子。