posted on 2023-01-12, 20:48authored byYan M.
H. Gonçalves, Bruno A. C. Horta
In the context of classical molecular
simulations, the accuracy
of a force field is highly influenced by the values of the relevant
simulation parameters. In this work, a parameter-space mapping (PSM)
workflow is proposed to aid in the calibration of force-field parameters,
based mainly on the following features: (i) regular-grid discretization
of the search space; (ii) partial sampling of the search-space grid;
(iii) training of surrogate models to predict the estimates of the
target properties for nonsampled parameter sets; (iv) post
hoc interpretation of the results in terms of multiobjective
optimization concepts; (v) attenuation of statistical errors achieved
via empiric extension of the duration of the simulations; (vi) iterative
search-space translation according to a user-defined scalar objective
function that measures the accuracy of the force field (e.g., the
weighted root-mean-square deviation of the target properties relative
to the reference data). This combination of features results in a
hybrid of a single- and a multiobjective optimization strategy, allowing
for the approximate determination of both a local minimum of the chosen
objective function and its neighboring Pareto efficient points. The
PSM workflow is implemented in the extensible Python program gmak, which is made available in the Git repository at http://github.com/mssm-labmmol/gmak. Using this implementation, the PSM workflow was tested in a proof-of-concept
fashion in the recalibration of the Lennard-Jones parameters of the
3-point Optimal Point Charge (OPC3) water model for compatibility
with the GROMOS treatment of nonbonded interactions. The recalibrated
model reproduces typical pure-liquid properties with an accuracy similar
to the original OPC3 model and represents a significant improvement
relative to the Simple Point Charge (SPC) model, which is the official
recommendation for simulations using GROMOS force fields.