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Several studies have been recently reported in the literature on sorptionproperties of MOFs with a number of organic sorbates, such as ethanol andmethanol. Surprisingly, still few studies have been reported on water sorbatedespite its large availability, low cost and environmental sustainability, andthe screening of a large number of hypothetical MOFs-water working pairs forengineering applications is still challenging. Based on a recently reporteddatabase of over 5000 hypothetical MOFs, a first contribution of this study isthe identification of the minimal set of crystallographic descriptorsunderpinning the most important sorption properties of MOFs for \ch{CO2} and,importantly, for \ch{H2O}. Furthermore, a comprehensive comparison of severalSequential Learning (SL) algorithms for MOFs properties optimization is carriedout and the role played by the above minimal set of crystallographicdescriptors clarified. In sorption-based energy transformations, thermodynamiclimits of important figures of merit (e.g. maximum specific energy) depend bothon operating conditions and equilibrium sorption properties in a wide range ofsorbate coverage values. The access to the latter properties is oftenincomplete, with essential quantities such as equilibrium adsorption isothermsspanning over the full sorbate coverage range and values of the isosteric heatbeing only partially available. As a result, this may prevent the computationof objective functions during the optimization procedure. We propose a fastprocedure for optimizing specific energy in a closed sorption energy storagesystem with the only access to the water Henry coefficient at a fixedtemperature value and to the specific surface area.

Minimal set of crystallographic descriptors for sorption properties in  hypothetical Metal Organic Frameworks: Role in sequential learning  optimization