Weighted least square analysis method for free energy calculations

被引:5
|
作者
Hu, Dan [1 ,2 ]
Guan, Xiaoqing [3 ]
Wang, Yukun [3 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Math Sci, Inst Nat Sci, Shanghai 200240, Peoples R China
[2] Shanghai Jiao Tong Univ, MOE LSC, Shanghai 200240, Peoples R China
[3] Shanghai Jiao Tong Univ, Inst Nat Sci, Shanghai 200240, Peoples R China
关键词
weighted least square; free energy; rare event; umbrella sampling; Welsam; FINDING SADDLE-POINTS; DYNAMICS; MECHANISM;
D O I
10.1002/jcc.25580
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Free energy calculation is an efficient way for studying rare event dynamics. For a complex rare event dynamics, multiple reaction coordinates may be required to describe the transition path between equilibrium states. Theoretically, a one-dimensional sampling along the transition path can provide sufficient information to calculate the potential of mean force (PMF) along the transition path. In the widely used free energy analysis method Wham, the sample data are divided into a series of bins to calculate PMF. However, bin segmentation in Wham is coupled with the umbrella potentials applied in each window, because each umbrella potential is assumed to have a close value for all sample points in each bin. This coupling makes it difficult to perform one-dimensional bin segmentation along the transition path when multivariable umbrella potentials are used in sampling. Here, we develop a weighted least square analysis method (Welsam) to take the place of Wham for free energy analysis. In the new method Welsam, bin segmentation is decoupled from application of umbrella potentials. As a result, it becomes very convenient to perform one-dimensional bin segmentation and calculate one-dimensional PMF along the transition path. Our simulation results suggest that Welsam has a comparable statistical error with Wham. Furthermore, Welsam can be used to reduce waste of sample data obtained during exploration of reaction coordinates. (c) 2018 Wiley Periodicals, Inc.
引用
收藏
页码:2397 / 2404
页数:8
相关论文
共 50 条
  • [21] Application of weighted least square method to machine vision system
    Yang, Jian
    Lü, Nai-Guang
    Dong, Ming-Li
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2009, 17 (08): : 1870 - 1877
  • [22] The Weighted Least Square Method Applied to the Binary and Ternary AHP
    Nishizawa, Kazutomo
    Takahashi, Iwaro
    ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES, 2010, 4 : 91 - +
  • [23] Comparative analysis between the maxent and the weighted least square shape functions in a collocation meshless method
    Perazzo, F.
    Marchant, F.
    REVISTA INTERNACIONAL DE METODOS NUMERICOS PARA CALCULO Y DISENO EN INGENIERIA, 2017, 33 (3-4): : 290 - 298
  • [24] Power System State Estimation and Bad Data Analysis Using Weighted Least Square Method
    Vishnu, T. P.
    Viswan, Vidya
    Vipin, A. M.
    PROCEEDINGS OF 2015 IEEE INTERNATIONAL CONFERENCE ON POWER, INSTRUMENTATION, CONTROL AND COMPUTING (PICC), 2015,
  • [25] A least-square weighted residual method for level set formulation
    Choi, Hyoung Gwon
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 2012, 68 (07) : 887 - 904
  • [26] 3D Analysis of Magnetohydrodynamic Flow Employing Meshless Method Based on Weighted Least Square Method
    Matsuzawa, Shuhei
    Hirata, Katsuhiro
    Miyasaka, Fumikazu
    2016 IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (CEFC), 2016,
  • [27] A meshless Hermite weighted least-square method for piezoelectric structures
    Ma, Xiao
    Zhou, Bo
    Xue, Shifeng
    APPLIED MATHEMATICS AND COMPUTATION, 2021, 400
  • [28] Study of the grey Verhulst model based on the weighted least square method
    Tang, Liwei
    Lu, Yayun
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2020, 545
  • [29] Identification of interacting bad data in the framework of the weighted least square method
    Granelli, G. P.
    Montagna, M.
    ELECTRIC POWER SYSTEMS RESEARCH, 2008, 78 (05) : 806 - 814
  • [30] A fast algorithm of dynamic weighing based on weighted least square method
    Yuan, Mei-Leng
    Kong, Fan-Hao
    Journal of Computers (Taiwan), 2020, 31 (03) : 319 - 326