Intelligent Selection of Metal–Organic Framework Arrays for Methane Sensing via Genetic Algorithms

Gas sensor arrays,
also called electronic noses, use many chemically
diverse materials to adsorb and subsequently
identify gas species in complex mixture environments. Ideally these
materials should have maximally complementary adsorption profiles
to achieve the best sensing performance, but in practice they are
selected by trial-and-error. Thus current electronic noses do not
achieve optimal detection. In this work, we employ metal–organic
frameworks (MOFs) as sensing materials and leverage a genetic algorithm
to identify optimal combinations of them for detecting methane leaks
in air. We build on our previously reported computational design methodology,
which ranked MOF arrays by their Kullback–Liebler divergence
(KLD) values for probabilistically describing the concentrations of
each gas species in an unknown mixture. We ran the genetic algorithm
to find optimal MOF arrays of various sizes when selecting from a
library of 50 different MOF materials. The genetic algorithm was able
to accurately predict the best arrays of any desired size when compared
to brute-force screening. Thus, this search optimization can be integrated
into the efficient design of MOF-based electronic noses.