To rank the performanceof materials for a given carbon captureprocess, we rely on pure component isotherms from which we predictthe mixture isotherms. For screening a large number of materials,we also increasingly rely on isotherms predicted from molecular simulations.In particular, for such screening studies, it is important that theprocedures to generate the data are accurate, reliable, and robust.In this work, we develop an efficient and automated workflow for ameticulous sampling of pure component isotherms. The workflow wastested on a set of metal-organic frameworks (MOFs) and provedto be reliable given different guest molecules. We show that the couplingof our workflow with the Clausius-Clapeyron relation savesCPU time, yet enables us to accurately predict pure component isothermsat the temperatures of interest, starting from a reference isothermat a given temperature. We also show that one can accurately predictthe CO2 and N-2 mixture isotherms using idealadsorbed solution theory (IAST). In particular, we show that IASTis a more reliable numerical tool to predict binary adsorption uptakesfor a range of pressures, temperatures, and compositions, as it doesnot rely on the fitting of experimental data, which typically needsto be done with analytical models such as dual-site Langmuir (DSL).This makes IAST a more suitable and general technique to bridge thegap between adsorption (raw) data and process modeling. To demonstratethis point, we show that the ranking of materials, for a standardthree-step temperature swing adsorption (TSA) process, can be significantlydifferent depending on the thermodynamic method used to predict binaryadsorption data. We show that, for the design of processes that captureCO(2) from low concentration (0.4%) streams, the commonlyused methodology to predict mixture isotherms incorrectly assignsup to 33% of the materials as top-performing.