An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulation.
An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulation.
复制标题
改进的统计分析，用于通过吉布斯系综蒙特卡罗模拟预测临界温度和临界密度。
DOI：
10.1063/1.4928865
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
W. Wilding
中科院分区：
文献类型：
--
作者：
Richard A. Messerly;R. L. Rowley;T. Knotts;W. Wilding
对Gibbs集合蒙特卡罗模拟进行了严格的统计分析。与文献中发现的传统方法相比，该分析减少了临界点估计的不确定性。由于以下结果，建议进行两种不同的改进。首先，传统的误差传播方法用于估计回归中使用的标准差，由于蒸汽和液体密度的内在相互依赖性，不正确地对目标函数中的项进行加权。因此，开发了一个误差模型来预测标准偏差。其次，也是最重要的是，与传统的线性化方程和传播斜率和截距误差的方法相比，采用了一种严格的非线性回归算法。传统的回归方法可以得到临界常数的非物理置信区间。相比之下，严格的算法将置信区域限制为物理上可感知的值。为了证明这些结论的有效性，我们进行了一个实例研究，以提高分子模拟的可靠性，以解决临界温度和临界密度下的正构烷烃族趋势。
A rigorous statistical analysis is presented for Gibbs ensemble Monte Carlo simulations. This analysis reduces the uncertainty in the critical point estimate when compared with traditional methods found in the literature. Two different improvements are recommended due to the following results. First, the traditional propagation of error approach for estimating the standard deviations used in regression improperly weighs the terms in the objective function due to the inherent interdependence of the vapor and liquid densities. For this reason, an error model is developed to predict the standard deviations. Second, and most importantly, a rigorous algorithm for nonlinear regression is compared to the traditional approach of linearizing the equations and propagating the error in the slope and the intercept. The traditional regression approach can yield nonphysical confidence intervals for the critical constants. By contrast, the rigorous algorithm restricts the confidence regions to values that are physically sensible. To demonstrate the effect of these conclusions, a case study is performed to enhance the reliability of molecular simulations to resolve the n-alkane family trend for the critical temperature and critical density.
荧光波动测量的浓度和亮度的置信区间。
影响因子：
3.4
作者：
Pryse,KennethM;Rong,Xi;Whisler,JordanA;McConnaughey,WilliamB;Jiang,Yan-Fei;Melnykov,ArtemV;Elson,ElliotL;Genin,GuyM
通讯作者：
Genin,GuyM