  Human breath contains over 3000 volatile organic compounds, abnormal
concentrations of which can indicate the presence of certain diseases.
Recently, metal-organic framework (MOF)-metal oxide composite materials have
been explored for chemiresistive sensor applications, however their ability to
detect breath compounds associated with specific diseases remains unknown. In
this work, we present a new high-throughput computational protocol for
evaluating the sensing ability of MOF-metal oxide towards small organic
compounds. This protocol uses a cluster-based method for accelerated structure
relaxation, and a combination of binding energies and density-of-states
analysis to evaluate sensing ability, the latter measured using Wasserstein
distances. We apply this protocol to the case of the MOF-metal oxide composite
material NM125-TiO2 and show that it is consistent with previously reported
experimental results for this system. We examine the sensing ability of
NM125-TiO2 for over 100 human-breath compounds spanning 13 different diseases.
Statistical inference then allows us to identifies ones which subsequent
experimental efforts should focus on. Overall, this work provides new tools for
computational sensor research, while also illustrating how computational
materials science can be integrated into the field of preventative medicine.
