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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 ofatomistic and ensemble motion. Coarse-grained models are therefore used inplace of costly atomistic simulations, allowing simulation of longer timescales and larger systems. Nevertheless, constructing the many-body potentialof mean force that defines the structure and dynamics of a coarse-grainedsystem can be complicated and computationally intensive. Machine learning showsgreat promise for the key coupled challenges of dimensionality reduction andlearning the potential of mean force. To improve the coarse-graining of ILs, wepresent a neural network model trained on all-atom classical molecular dynamicssimulations. The potential of mean force is expressed as two jointly-trainedneural network interatomic potentials that learn the coupled short-range andthe many-body long range molecular interactions. These interatomic potentialstreat temperature as an explicit input variable to capture the temperaturedependence of the potential of mean force. The model reproduces structuralquantities with high fidelity, outperforms the temperature-independent baselineat capturing dynamics, generalizes to unseen temperatures, and incurs lowsimulation cost.

Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks