To accelerate the pace of development and provide cost-effective solutions for carbon capture applications, the materials and process engineering research communities have shifted from assessing a limited number of structures at a time towards screening entire databases for evaluating the performance of hundreds to millions of solid sorbents. In some of these studies, simple metrics such as selectivity were used to evaluate the performance of the materials. In a more recent study, a vacuum swing adsorption process was developed and materials were ranked based on process indicators such as purity, recovery, parasitic energy, and productivity. These screening studies consider mainly binary mixtures of CO 2 and N 2 in the gas stream. Therefore, the accurate prediction of binary adsorption data plays a key role in evaluating the performance of materials in carbon capture processes. Consequently, the development of reliable and accurate tools to predict mixture adsorption data is required to link materials' adsorption properties to their process performance. So far, in computational screening studies, the prediction of mixture adsorption isotherms is usually done by fitting the simulated isotherms for the pure components to one specific mixture adsorption model. Moreover, for process optimization purposes, mixture adsorption data is required at many different operating conditions. It is therefore important that one selects the right adsorption isotherm model such that the data can be described sufficiently accurately. Likewise, if experimental data is used to describe the thermodynamics of an adsorption process, a lot of effort is spent in fitting that data to the right model. This cumbersome fitting process can not be utilized on screening studies of hundreds or thousands of materials, hence the need for a robust workflow that gives us an optimal prediction of the mixture adsorption isotherms for a wide range of pressures, temperatures, and compositions.