The second, more general, charging approach is to employ graph neural networks (GNNs) to perceive chemical environments, which assign molecular parameters for diverse chemistries in linear time with respect to molecule size. The ease-of-use and prevalence of the OpenFF force fields and parameterization pipeline has led to the development of numerous GNN force field and molecular parameterization models which can directly interface with OpenFF workflows. Selected examples of such GNN models include: