posted on 2021-12-20, 18:45authored byIbrahim
B. Orhan, Hilal Daglar, Seda Keskin, Tu C. Le, Ravichandar Babarao
Machine
learning (ML), which is becoming an increasingly popular
tool in various scientific fields, also shows the potential to aid
in the screening of materials for diverse applications. In this study,
the computation-ready experimental (CoRE) metal–organic framework
(MOF) data set for which the O2 and N2 uptakes,
self-diffusivities, and Henry’s constants were calculated was
used to fit the ML models. The obtained models were subsequently employed
to predict such properties for a hypothetical MOF (hMOF) data set
and to identify structures having a high O2/N2 selectivity at room temperature. The performance of the model on
known entries indicated that it would serve as a useful tool for the
prediction of MOF characteristics with r2 correlations between the true and predicted values typically falling
between 0.7 and 0.8. The use of different descriptor groups (geometric,
atom type, and chemical) was studied; the inclusion of all descriptor
groups yielded the best overall results. Only a small number of entries
surpassed the performance of those in the CoRE MOF set; however, the
use of ML was able to present the structure–property relationship
and to identity the top performing hMOFs for O2/N2 separation based on the adsorption and diffusion selectivity.