DE-FF performs well on enthalpy of mixing and transfer free energy benchmarks, but we did not train on properties that depend on vacuum energies (such as heats of vapourisation). We can extract the component aqueous and non-aqueous solvation free energies from the condensed phase benchmark set for DE-FF, and find RMS errors of 1.39 1.25 and 1.07 1.15 0.99 kcal/mol, respectively. For comparison, GAFF and Sage have RMS errors in hydration free energy of 0.93 0.83 and 1.01 1.13 0.88 kcal/mol, respectively (Section S3). DE-FF inherits the fixed-charge, non-polarisable AM1-BCC charge model that is used with LJ force field models, and so co-training of electrostatic and DE parameters would be interesting to further improve the accuracy of solvation free energies. However, in this first version, we are content to target transfer properties for use in condensed phase modelling. Timing and convergence. Sampling performance was investigated using our implementation of the DE-FF in the OpenMM custom force class, which provides just-in-time compilation for energy and force calculations on CPUs or GPUs . For an ethanol molecule solvated in 1000 (4-point) water molecules, we obtain throughput of 185 ns/day with a Tesla V100 32G NVLink 2.0. Thus, run times are on the same order as similar calculations using the Sage force field with a soft core LJ potential for the ethanol molecule (255 ns/day), and significantly faster than a previously-reported CPU-only implementation in pmemd . We expect further optimisation to be possible, given that LJ interactions are computed using dedicated and optimised CUDA code in OpenMM. Similar optimisations could be performed for the DE functional form.