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

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, and ΔHv. 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, and ΔHv in 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, and ΔHv for force field j by simulating force field i, where i and j are 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).

直方图重加权法（Histogram reweighting, HR）是将巨正则系综蒙特卡洛（grand canonical Monte Carlo, GCMC）模拟结果转换为气液共存性质——涵盖饱和液相密度（ρliqsat）、饱和气相密度（ρvapsat）、饱和蒸气压（Pvapsat）及汽化焓（ΔHv）——的标准方法。本研究证实，无直方图重加权方法即多态贝内特接受比法（Multistate Bennett Acceptance Ratio, MBAR），在计算上述ρliqsat、ρvapsat、Pvapsat与ΔHv时，与传统HR方法的计算结果具有统计学一致性。MBAR的核心优势在于，可直接预测未经过直接模拟的任意力场参数集的相平衡性质，因此能够大幅减少利用相平衡数据对力场进行参数化所需的GCMC模拟次数。本研究共展示了GCMC-MBAR的四类典型应用：其一，在极限测试案例中验证了GCMC-MBAR与GCMC-HR在计算上述四项参数时所得结果在统计层面无显著差异；其二，基于相平衡用米势（Mie Potentials for Phase Equilibria, MiPPE）力场，借助GCMC-MBAR为8种支链烷烃与11种炔烃优化了专属（化合物特异性）参数ψ；其三，通过对力场i进行模拟，预测力场j的ρliqsat、ρvapsat、Pvapsat及ΔHv，其中i与j均为已发表文献中的常用力场，此外本研究还提供了判断GCMC-MBAR预测值可靠性的指导准则；其四，开发并应用了一种模拟后优化方案，为环己烷获取了全新的MiPPE非键相互作用参数（ϵCH2、σCH2与λCH2）。