Gaussian Markov transition models of molecular kinetics.
Gaussian Markov transition models of molecular kinetics.
复制标题
DOI：
10.1063/1.4913214
复制
发表时间：
2015-02
期刊：
影响因子：
--
通讯作者：
Hao Wu;F. Noé
中科院分区：
文献类型：
--
作者：
Hao Wu;F. Noé
分子动力学（MD）模拟的缓慢过程-由主导的本征值和本征函数的MD传播-包含的结构和过渡率之间的长寿构象的基本信息。现有的方法，这个问题，包括马尔可夫状态模型和变分的方法，代表了一组基函数的线性组合的主导特征函数。然而，基函数的选择及其系统的统计估计是一个尚未解决的问题。在这里，我们提出了一类新的动力学模型称为马尔可夫过渡模型（MTM），近似的MD传播的概率密度的混合物的过渡密度。具体来说，我们使用高斯MTM，其中高斯混合模型用于近似对称化的过渡密度。这种方法允许直接计算光谱分量。与其他Galerkin型近似相比，我们的方法可以自动调整所涉及的高斯基函数，并在贝叶斯框架中处理统计不确定性。通过仿真实例验证了该方法的有效性和准确性。
The slow processes of molecular dynamics (MD) simulations--governed by dominant eigenvalues and eigenfunctions of MD propagators--contain essential information on structures of and transition rates between long-lived conformations. Existing approaches to this problem, including Markov state models and the variational approach, represent the dominant eigenfunctions as linear combinations of a set of basis functions. However the choice of the basis functions and their systematic statistical estimation are unsolved problems. Here, we propose a new class of kinetic models called Markov transition models (MTMs) that approximate the transition density of the MD propagator by a mixture of probability densities. Specifically, we use Gaussian MTMs where a Gaussian mixture model is used to approximate the symmetrized transition density. This approach allows for a direct computation of spectral components. In contrast with the other Galerkin-type approximations, our approach can automatically adjust the involved Gaussian basis functions and handle the statistical uncertainties in a Bayesian framework. We demonstrate by some simulation examples the effectiveness and accuracy of the proposed approach.