Sriraman Saravanapriyan, Kevrekidis Ioannis G, Hummer Gerhard
Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544, USA.
J Phys Chem B. 2005 Apr 14;109(14):6479-84. doi: 10.1021/jp046448u.
We use Bayesian inference to derive the rate coefficients of a coarse master equation from molecular dynamics simulations. Results from multiple short simulation trajectories are used to estimate propagators. A likelihood function constructed as a product of the propagators provides a posterior distribution of the free coefficients in the rate matrix determining the Markovian master equation. Extensions to non-Markovian dynamics are discussed, using the trajectory "paths" as observations. The Markovian approach is illustrated for the filling and emptying transitions of short carbon nanotubes dissolved in water. We show that accurate thermodynamic and kinetic properties, such as free energy surfaces and kinetic rate coefficients, can be computed from coarse master equations obtained through Bayesian inference.
我们使用贝叶斯推理从分子动力学模拟中推导粗粒主方程的速率系数。多个短模拟轨迹的结果用于估计传播子。作为传播子乘积构建的似然函数提供了确定马尔可夫主方程的速率矩阵中自由系数的后验分布。讨论了使用轨迹“路径”作为观测值对非马尔可夫动力学的扩展。以溶解在水中的短碳纳米管的填充和排空转变为例说明了马尔可夫方法。我们表明，可以从通过贝叶斯推理获得的粗粒主方程计算出准确的热力学和动力学性质，如自由能表面和动力学速率系数。
