To explore these issues, we carry out a multiscale HTCS where we assess the suitability of six FFs commonly chosen to simulate CO2 and N2 adsorption in MOFs. Our goal is to understand how the material rankings are impacted by this choice, not necessarily on how well the FFs reproduce experimental data, and so we deliberately avoid making any comparisons to experimental measurements of adsorption. As a process-level case study, we consider the separation of CO2 (15 vol%) and N2 (85 vol%) binary mixtures using a modified Skarstrom PSA cycle at conditions representative of dry flue gas streams from coal-fired power plants. We generate distributions of CO2 and N2 adsorption isotherms in 690 MOFs at multiple temperatures using LJ parameters from either Dreiding or UFF, in combination with partial charges assigned by the DDEC, EQeq, or Qeq schemes, as described in sections 2.1 and 2.2. We then determine the distributions in process-level performances for every MOF using a combination of high-fidelity PSA process modelling and machine learning (sections 2.3 and 2.4). Using these data, we generate material ranking lists Figure . An approach to understanding the impact of molecular forcefields on the computational ranking of MOFs for CO2 capture. In the first step, a subset of molecular forcefields (comprised of Lennard-Jones parameters and partial charge assignment schemes) are chosen for evaluation. In the second step, adsorption data is generated using different forcefields via molecular simulation. The rankings of MOFs are then compared using material-level metrics such as CO2 uptake. Until now, the differences between forcefields have only been evaluated at this level. In the third step, as in the current study, the impact of one's choice in forcefield is evaluated using rigorous process simulation and optimisation.