A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture.To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms.  For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations.  In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms.  We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature.  We also show that one can accurately predict the CO2 and N2 mixture isotherms using ideal adsorbed solution theory (IAST).  To demonstrate this point, we show that the ranking of materials, for a standard three-step temperature swing adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data.  We show that, for the design of processes that capture CO2 from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.
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