Energy Department, Politecnico di Torino, Turin 10129, Italy.
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany.
Proc Natl Acad Sci U S A. 2017 Jul 11;114(28):E5494-E5503. doi: 10.1073/pnas.1621481114. Epub 2017 Jun 20.
We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.
我们描述并实现了一种计算机辅助方法，用于加速探索未知的有效自由能表面（FES）。更一般地说，目标是从随机或原子模拟中提取粗粒度的宏观信息，例如分子动力学（MD）。该方法在功能上将 MD 模拟器与非线性流形学习技术联系起来。通过利用 FES 逐渐揭示的内在低维几何结构的光滑性，对模拟器进行偏向于未探索相空间区域的偏置，从而增加了方法的附加值。
