Configuration-Sampling-Based Surrogate Models for Rapid Parameterization of Non-Bonded Interactions

In
this study, we present an approach for rapid force field parameterization
and uncertainty quantification of the non-bonded interaction parameters
for classical force fields. The accuracy of most thermophysical properties,
and especially vapor–liquid equilibria (VLE), obtained from
molecular simulation depends strongly on the non-bonded interactions.
Traditionally, non-bonded interactions are parameterized to agree
with macroscopic properties by performing large amounts of direct
molecular simulation. Due to the computational cost of molecular simulation,
surrogate models (i.e., efficient models that approximate direct molecular
simulation results) are an essential tool for high-dimensional parameterization
and uncertainty quantification of non-bonded interactions. The present
study compares two different configuration-sampling-based surrogate
models, namely, Multistate Bennett Acceptance Ratio (MBAR) and Pair
Correlation Function Rescaling (PCFR). MBAR and PCFR are coupled with
the Isothermal Isochoric (ITIC) thermodynamic integration method for
estimating vapor–liquid saturation properties. We find that
MBAR and PCFR are complementary in their roles. Specifically, PCFR
is preferred when exploring distant regions of the parameter space
while MBAR is better in the local domain.