  Computer simulations can provide mechanistic insight into ionic liquids (ILs)
and predict the properties of experimentally unrealized ion combinations.
However, ILs suffer from a particularly large disparity in the time scales of
atomistic and ensemble motion. Coarse-grained models are therefore used in
place of costly atomistic simulations, allowing simulation of longer time
scales and larger systems. Nevertheless, constructing the many-body potential
of mean force that defines the structure and dynamics of a coarse-grained
system can be complicated and computationally intensive. Machine learning shows
great promise for the key coupled challenges of dimensionality reduction and
learning the potential of mean force. To improve the coarse-graining of ILs, we
present a neural network model trained on all-atom classical molecular dynamics
simulations. The potential of mean force is expressed as two jointly-trained
neural network interatomic potentials that learn the coupled short-range and
the many-body long range molecular interactions. These interatomic potentials
treat temperature as an explicit input variable to capture the temperature
dependence of the potential of mean force. The model reproduces structural
quantities with high fidelity, outperforms the temperature-independent baseline
at capturing dynamics, generalizes to unseen temperatures, and incurs low
simulation cost.

Computational Physics;Materials Science