To illustrate the flexibility of the DE functional form, and the utility of smirnoff-plugins (Section 4.1) interfaced with the OpenFF software stack, we re-fit a new DE-based four-point water model. The starting parameters for the training run come from a curve fit of the DE α, β , ε and r m parameters (eq 2) to the trained O-O potential parameters from a literature B68 model (Figure ) . We name this model DE-B68 to highlight the origin of the starting parameters and to distinguish it from the earlier DE-TIP3P water model . Unlike the DE-TIP3P wa-ter model, all parameters of the DE-B68 water model, along with the atomic partial charges and O-X distance, were re-optimised automatically using ForceBalance against a wide range of experimental properties. The experimental target training data include the liquid density (ρ), heat of vaporization (∆H vap ), thermal expansion coefficient (α), isothermal compressibility (κ T ), isobaric heat capacity (C p ) and the dielectric constant (ε(0)) at a range of temperatures and pressures (Section 4.2).