Let us first look in more detail at the steps and the assumptions typically involved in HTCS workflows. Generally speaking, HTCS can be conducted at the material level or the process level. Material-level screening studies filter MOFs for a desired property. For example, CO2 / N2 adsorption selectivity has been used as a key performance indicator (KPI) to organise MOF databases for CO2 capture applications on several occasions. However, for complex, dynamic processes such as PSA, this property alone is unlikely to provide enough information to determine the separation potential of an adsorbent. For that reason, process-level screening workflows must be utilised. This approach relies on atomistic Grand Canonical Monte Carlo (GCMC) simulations to obtain microscale properties of the adsorbent (in particular, equilibrium adsorption data). This data is then passed onto detailed PSA process models whereby MOFs are evaluated under realistic separation conditions. At this level, we are interested in KPIs which reflect the economic drivers of the separation, such as high CO2 product purity and recovery, or low energy penalty (energy required to capture a unit amount of CO2) and high productivity (how much CO2 is captured per kg of adsorbent per second). These properties depend on the properties of the adsorbent as well as the variables of the PSA cycle (pressures of each step, duration of each step, etc.). However, there is no unique combination of the PSA cycle variables which satisfies all of the processlevel KPIs simultaneously, and so the trade-off between competing objectives can be represented as a series of Pareto-optimal solutions known as a Pareto front. Comparing Pareto fronts for two different materials has therefore become an established protocol for ranking MOFs based on their performance. Among the several design choices one is posed with when constructing a process-level simulation pipeline, the first (and perhaps most important) choice is how to generate the adsorption data using molecular simulations.