The next question we pose is whether the high level of consistency in the CL1 group is because the adsorption behaviours in these materials are insensitive to the parameters of the FF or due to some other factors. To address this question, in Figure (a) we visualise the relative ranking of CL1 class MOFs, using a box-and-whisker plot to represent the underlying distributions in process performances. It is evident from this subplot that the CL1 class contains materials which are characterised by both narrow distributions in performance, i.e., PEKKAS, and broad distributions in performance, i.e., NOQLOV. This suggests that a search for materials which consistently meet the CCS constraints is not sufficient to guarantee that their performance is insensitive to the choice of FF. Still, within the CL1 subset there are materials such as PEKKAS, QOVWOO, FIRVEH, and NAYZUK, whose narrow boxplots have the potential to confirm our hypothesis. By inspection of the individual adsorption behaviours and performance curves for select materials in Figure (b), we observe a diversity of responses to the choice of FF. In every case however, some scattering in the adsorption data is observed. This means that the MOFs which are (rather fortuitously) characterised by narrow performance distributions achieve this level of consistency through different pathways. That is to say, the CO2 purity-recovery Pareto fronts in these materials converge to similar high performing solutions (despite the scattering seen in the adsorption data) by using very different combinations of the PSA cycle parameters. Therefore, the results in Figure do not provide us with the type of evidence one would like to confirm our initial hypothesis. We arrive then to the conclusion that, in order to achieve consistent process performances in multiscale HTCS workflows for the right reasons, the inconsistencies which propagate from the material level must first be addressed. This brings us onto our second idea. Here, we test whether the uncertainties which are introduced by charge equilibration schemes can be effectively mitigated by using the MLbased MPNN and PACMOF models. As a case study, we continue with the CL1 class of MOFs. These materials satisfy the CCS constraints across all FFs, and so we evaluate the accuracy of MPNN and PACMOF charges by optimising the energy penalty and productivity performances subject to the CCS constraints. This protocol is more robust than an unconstrained CO2 purity-recovery optimisation as the energy-productivity process responses are more sensitive to changes in the adsorption behaviours. The optimisations were first conducted using the ANN model and then refined using the PSA process model in order to calculate the hypervolume errors between Pareto fronts generated using different Disregarding the MPNN and PACMOF charges in Figure for a moment, we observe a similar response in the energy-productivity results to changes in the FF as the CO2 purity-recovery results. However, it is clear that scattering in the adsorption data has a stronger influence on these KPIs. For instance, the average hypervolume error of 24% between LJ FFs (combined with DDEC charges) in Figure represents a 3-fold increase in the errors calculated at the CO2 purity-recovery Pareto level (Figure ). While these results are determined for a reduced subset of MOFs, we generally expect an uncertainty of approximately 30% between LJ FFs using energy-productivity KPIs (see supplementary note 8.3). Upon introducing the MPNN and PACMOF charges back into the discussion, the lowest uncertainties in Figure no longer occur between UFF and Dreiding using fixed DDEC charges. Instead, they occur between DDEC and MPNN / PACMOF charges using fixed UFF parameters, meaning that the prevailing sources of uncertainty identified previously (the choice of charge) has been effectively mitigated by modelling the electrostatics with MLbased charges. Interestingly, the uncertainty that is introduced by the MPNN and PACMOF models is comparable to the scattering seen between surrogate model predictions and PSA process simulations (Figure ). Efficient pre-screening of MOF databases could therefore be achieved by combining ML models for both the partial charge calculations and the PSA cycle optimisations without a significant loss of accuracy. Overall, our results suggest that MPNN and PACMOF can be used to assign charges in lieu of the DDEC method (see Figures and). This is further demonstrated in Figure whereby we see that, of the materials shown here, the adsorption behaviours and process-level predictions obtained using ML-based charges are in closer agreement with the DDEC baseline for all materials apart from NOQLOV.