The slow progress towards extending this approach to a transferable HAIEFF force-field may stem from two reasons. Firstly, to our knowledge there does not currently exist an appropriate, open-source database of synthetic data suitable for force-field parameterization. The computational cost of generating a suitable database is considerable given that transferability requires such a dataset to cover a wide variety of molecular systems without compromising on the accuracy of the theoretical calculations. In this context, HAIEFF models benefit from the fact the empirical force-field attempts to describe only the residual interactions and thus requires fewer fitting parameters. The other parameters describing the intramolecular and electrostatic energy are derived independently from ab initio calculations on the system of interest. This means that the quantity of reference data required to derive reliable parameter estimates is significantly reduced. In contrast, a TMFF that attempts to parameterize empirical descriptors for all interactions in the crystal would require a more extensive dataset. As an example, when a TMFF was parameterized for a relatively small molecule like cyclohexane-1,4-dione (16 atoms), a combination of 157 crystal and conformational data points were employed . 89 of these data points were used for fitting the intramolecular and electrostatic interactions that would have otherwise been excluded from the empirical parts of the HAIEFF model. Moreover, transferable HAIEFF force-fields might be more data-hungry than their molecule-specific counterparts, but once generated the reference data can be re-used in all future transferable HAIEFF force-field parameterizations making it an investment that is 'repaid' over time. Conversely, in molecule-specific models, each set of synthetic reference data generated is a sunk cost for that particular system.