Wang Lingle, Abel Robert, Friesner Richard A, Berne B J
Department of Chemistry, Columbia University, New York, NY, 10027,
J Chem Theory Comput. 2009 Jun 9;5(6):1462-1473. doi: 10.1021/ct900078k.
Due to its fundamental importance to molecular biology, great interest has continued to persist in developing novel techniques to efficiently characterize the thermodynamic and structural features of liquid water. A particularly fruitful approach, first applied to liquid water by Lazaridis and Karplus, is to use molecular dynamics or Monte Carlo simulations to collect the required statistics to integrate the inhomogeneous solvation theory equations for the solvation enthalpy and entropy. We here suggest several technical improvements to this approach, which may facilitate faster convergence and greater accuracy. In particular, we devise a nonparametric k'th nearest neighbors (NN) based approach to estimate the water-water correlation entropy, and suggest an alternative factorization of the water-water correlation function that appears to more robustly describe the correlation entropy of the neat fluid. It appears that the NN method offers several advantages over the more common histogram based approaches, including much faster convergence for a given amount of simulation data; an intuitive error bound that may be readily formulated without resorting to block averaging or bootstrapping; and the absence of empirically tuned parameters, which may bias the results in an uncontrolled fashion.
由于其对分子生物学的根本重要性，人们一直对开发新技术以有效表征液态水的热力学和结构特征保持着浓厚兴趣。一种特别富有成效的方法，最初由拉扎里迪斯和卡尔普斯应用于液态水，是使用分子动力学或蒙特卡罗模拟来收集所需的统计数据，以积分非均匀溶剂化理论方程来计算溶剂化焓和熵。我们在此提出对该方法的若干技术改进，这可能有助于更快地收敛并提高准确性。特别是，我们设计了一种基于非参数第k个最近邻（NN）的方法来估计水 - 水相关熵，并提出了水 - 水相关函数的另一种因式分解，它似乎能更稳健地描述纯流体的相关熵。与更常见的基于直方图的方法相比，NN方法似乎具有几个优点，包括对于给定数量的模拟数据收敛速度更快；可以在不借助块平均或自助法的情况下轻松制定直观的误差界限；并且不存在经验调整参数，这些参数可能以不受控制的方式使结果产生偏差。
