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.