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On the calculation of absolute free energies from molecular-dynamics or Monte Carlo data

机译:从分子动力学或蒙特卡洛数据计算绝对自由能

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We propose a method for calculating absolute free energies from Monte Carlo or molecular-dynamics data.The method is based on the identity that expresses the partition function Q as a Boltzmann average: l/Q = ,where w(p,x) is an arbitrary weight function such that its integral over the phase space is equal to 1.In practice,to minimize statistical errors the weight function is chosen such that the regions of the phase space where sampling statistics are poor are excluded from the average.The "ideal" weight function would be the equilibrium phase-space density exp[-betaH(p,x)]/Q itself.We consider two methods for constructing the weight function based on different estimates of the equilibrium phase-space density from simulation data.In the first method,it is chosen to be a Gaussian function,whose parameters are obtained from the covariance matrix of the atomic coordinates.In the second,a clustering algorithm is used to attempt partitioning the data into clusters corresponding to different basins of attraction visited by the system.The weight function is then constructed as a superposition of Gaussians calculated for each cluster separately.We show that these strategies can be used to improve upon previous methods of estimating absolute entropies from covariance matrices.
机译:我们提出了一种从蒙特卡洛(Monte Carlo)或分子动力学数据计算绝对自由能的方法,该方法基于将分配函数Q表示为玻尔兹曼平均值的恒等式:l / Q = ,其中w(p,x)是任意权重函数,这样它在相空间上的积分等于1。实际上,为了使统计误差最小,选择权重函数使得理想的权函数是平衡相空间密度exp [-betaH(p,x)] / Q本身。我们考虑了两种构造权重的方法函数是根据模拟数据对平衡相空间密度的不同估计得出的函数。第一种方法是选择高斯函数,其参数是从原子坐标的协方差矩阵中获得的。第二种方法是聚类算法用于尝试将数据分区到群集中然后,系统将权重函数构造为分别为每个聚类计算的高斯叠加。我们证明,这些策略可用于改进先前从协方差矩阵估计绝对熵的方法。

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