While we strongly advocate for ab initio charges whenever possible, the results presented in Figure and Figure support our argument that ML-based charges are the next best option available to practitioners. Nonetheless, we recognise some of the shortcomings in our analysis (see supplementary note 9) and understand that 28 MOFs cannot represent the diverse chemical landscape of all known MOFs. It is likely that expanding the scope of our study to more materials would expose some regions of poor agreement between ML-based and DDEC charges. Indeed, Liu and Luan recently benchmarked several charge assignment methods and determined that charge predictions from PACMOF significantly deteriorated in MOFs which contained transition metals. However, as larger MOF databases with pre-computed ab initio charges begin to emerge, we expect that improvements in the current ML models can be achieved by simply extending the training set to cover more materials with greater chemical diversity. Future efforts should A problem still remains when we are posed with the question of which LJ FF to use.