Histogram-Free Reweighting with Grand Canonical Monte Carlo: Post-simulation Optimization of Non-bonded Potentials for Phase Equilibria

Histogram-Free Reweighting with Grand Canonical Monte Carlo: Post-simulation Optimization of Non-bonded Potentials for Phase Equilibria
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使用大正则蒙特卡罗进行无直方图重加权:相平衡非键势的仿真后优化

DOI:
10.1021/acs.jced.8b01232
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发表时间:
2019
影响因子:
--
通讯作者:
Shirts, Michael R.
Shirts, Michael R.
中科院分区:
工程技术3区
文献类型:
--
作者:
Messerly, Richard A.;Soroush Barhaghi, Mohammad;Potoff, Jeffrey J.;Shirts, Michael R.

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直方图加权(HR)是将宏正则蒙特卡罗(GCMC)模拟输出转换为汽液共存性质(饱和液体密度、ρ液体密度、饱和蒸气密度、ρ蒸汽量、饱和蒸汽压、PVAPSAT和蒸发热ΔHv)的标准方法。我们证明了一种无直方图的重加权方法,即多态贝内特接受率(MBAR),类似于计算ρliqsat、ρVapsat、PVapsat和ΔHv的传统HR方法。MBAR的主要优点是能够预测尚未直接模拟的任意力场参数集的相平衡性质。因此,MBAR可以大大减少用相平衡数据对力场进行参数化所需的GCMC模拟次数。本研究介绍了GCMC-MBAR的四种不同应用。首先,我们验证了GCMC-MBAR和GCMC-HR对于ρliqsat、ρVapsat、PVapsat和ΔHvin在一个极限测试案例中产生了统计上不可区分的结果。其次,我们利用Mie相平衡势力场优化了8个支链烷烃和11个炔烃的个体化(化合物专有)参数(ψ)。第三,我们通过模拟力场Di来预测ρLiqsat、ρVapsat、PVapsat和ΔHvj的力场,其中和j1是文献中常见的力场。此外,我们还提供了确定GCMC-MBAR预测值可靠性的指南。第四,我们开发并应用了一种后模拟优化方案来获得环己烷(ϵCH2、σCH2和λCH2)的新的MPPE非键参数。
Histogram reweighting (HR) is a standard approach for converting grand canonical Monte Carlo (GCMC) simulation output into vapor–liquid coexistence properties (saturated liquid density, ρliqsat, saturated vapor density, ρvapsat, saturated vapor pressures,Pvapsat, and enthalpy of vaporization,ΔHv). We demonstrate that a histogram-free reweighting approach, namely, the Multistate Bennett Acceptance Ratio (MBAR), is similar to the traditional HR method for computing ρliqsat, ρvapsat,Pvapsat, andΔHv. The primary advantage of MBAR is the ability to predict phase equilibria properties for an arbitrary force field parameter set that has not been simulated directly. Thus, MBAR can greatly reduce the number of GCMC simulations that are required to parameterize a force field with phase equilibria data. Four different applications of GCMC-MBAR are presented in this study. First, we validate that GCMC-MBAR and GCMC-HR yield statistically indistinguishable results for ρliqsat, ρvapsat,Pvapsat, andΔHvin a limiting test case. Second, we utilize GCMC-MBAR to optimize an individualized (compound-specific) parameter (ψ) for 8 branched alkanes and 11 alkynes using the Mie Potentials for Phase Equilibria (MiPPE) force field. Third, we predict ρliqsat, ρvapsat,Pvapsat, andΔHvfor force fieldjby simulating force fieldi, whereiandjare common force fields from the literature. In addition, we provide guidelines for determining the reliability of GCMC-MBAR predicted values. Fourth, we develop and apply a post-simulation optimization scheme to obtain new MiPPE non-bonded parameters for cyclohexane (ϵCH2, σCH2, and λCH2).
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