Porous carbons are
an important class of porous material for carbon
capture. The textural properties of porous carbons greatly influence
their CO2 adsorption capacities. But it is still unclear
what features are most conductive to achieving high CO2/N2 selectivity. Here, we trained deep neural networks
from the experimental data of CO2 and N2 uptakes
in porous carbons based on textural features of micropore volume,
mesopore volume, and BET surface area. We then used the model to screen
porous carbons and to predict CO2 and N2 uptakes,
as well as CO2/N2 selectivity. We found that
the highest CO2/N2 selectivity can be achieved
not at the regions of highest CO2 uptake but at the regions
of lowest N2 uptake where mesopores disrupt N2 adsorption. This insight will help guide experiments to synthesize
better porous carbons for post-combustion CO2 capture.