  Several studies have been recently reported in the literature on sorption
properties of MOFs with a number of organic sorbates, such as ethanol and
methanol. Surprisingly, still few studies have been reported on water sorbate
despite its large availability, low cost and environmental sustainability, and
the screening of a large number of hypothetical MOFs-water working pairs for
engineering applications is still challenging. Based on a recently reported
database of over 5000 hypothetical MOFs, a first contribution of this study is
the identification of the minimal set of crystallographic descriptors
underpinning the most important sorption properties of MOFs for \ch{CO2} and,
importantly, for \ch{H2O}. Furthermore, a comprehensive comparison of several
Sequential Learning (SL) algorithms for MOFs properties optimization is carried
out and the role played by the above minimal set of crystallographic
descriptors clarified. In sorption-based energy transformations, thermodynamic
limits of important figures of merit (e.g. maximum specific energy) depend both
on operating conditions and equilibrium sorption properties in a wide range of
sorbate coverage values. The access to the latter properties is often
incomplete, with essential quantities such as equilibrium adsorption isotherms
spanning over the full sorbate coverage range and values of the isosteric heat
being only partially available. As a result, this may prevent the computation
of objective functions during the optimization procedure. We propose a fast
procedure for optimizing specific energy in a closed sorption energy storage
system with the only access to the water Henry coefficient at a fixed
temperature value and to the specific surface area.
