Density of States and Binding Energy Informatics for Exploring Early Disease Detection in MOF-Metal Oxide Chemiresistive Sensors

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, a new high-throughput computational protocol for evaluating the sensing ability of MOF-metal oxide toward small organic compounds is presented. 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. This protocol is applied to the case of the MOF-metal oxide composite material NM125-TiO₂ and is shown to be consistent with previously reported experimental results for this system. The sensing ability of NM125-TiO₂ for over 100 human-breath compounds spanning 13 different diseases is examined. Statistical inference is then used to identify diseases 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.