To maximise transferability of the parameters across systems, it is usually beneficial to fit all parameters in the model simultaneously to prevent error propogation. This ensures, for instance, that the parameters for carbon-carbon interactions are well-conditioned for use beyond simple hydrocarbon systems, such as in the structures of azo-organic or oxy-organic compounds. This has not been done by previous researchers in this area most likely due to the inherent computational complexity. Our implementation is suited for such large-scale problems and optimisation of all model parameters simultaneously can be performed more easily. This drastically reduces the number of separate parameter estimations that need to be performed to obtain a full parameter set. While it is hoped that this approach will improve the quality of the resultant parameter estimates, there are cases where it might do otherwise. In the HAIEFF model employed in this work, the effects of induction have not been considered. Without this key contributor to hydrogen bonding effects, force-field parameters that should be unrelated to hydrogen bonding may be wrongly used to compensate for the omitted energy term. This could ultimately hurt the transferability of the final parameter sets. Careful selection of the weighting factors in Equation can aid in abating this problem. Since our primary objective in this paper is to test the parameter estimation algorithm, rather than to generate a generic parameter set, the energy weightings for structures containing O-H or N-H bonds are set to zero (i.e., w e s = 0), while the geometry weightings are fixed at unity (i.e., w g s = 1). In this manner, energy residuals for structures that can form hydrogen bonds are excluded from the parameter fitting and there is less risk of the previously described error propagation. For all other structures, we select an unbiased weighting scheme (i.e., w g s = w e s = 1) as applied by other authors . Bowskill has shown by k-fold validation that the force-fields generated with such a weighting scheme exhibit only minor deterioration in accuracy when used to predict crystal geometries and energies beyond the training data, further justifying this selection .