Tremendous progress has been achieved in the past decade developing data driven surrogate models for learning molecular potential energy surfaces (PES) 1-3 . Data-driven models are functional forms with high dimensionality, typically containing between ∼ 10 4 to ∼ 10 5 parameters, that are machinelearned from from ab initio training datasets. The highdimensionality of data-driven models allows for the capture of complex intermolecular potentials with ab initio accuracy that cannot be fit to low dimensional (∼ 10 2 ) empirical interatomic potentials . Further, data-driven models can bypass the spatio-temporal limitations of density functional theory (DFT) based molecular dynamics (MD) simulations by dramatically reducing the associated computational cost of dynamical simulations. Many data-driven model architectures have been developed over the years, with some of the most relevant being Behler-Parrinello neural network, Gaussian approximation potential (GAP), Spectral Neighbor Analysis Potential (SNAP) , moment tensor potential (MTP), ANI, FCHL, SchNet, MBTR, DeepMD, linear atomic cluster expansion and NequIP . Taken together, these classes of data-driven intermolecular potentials facilitate a new frontier for ab initio quality dynamical modeling at unprecedented spatio-temporal scales.