  Although the tailored metal active sites and porous architectures of MOFs
hold great promise for engineering challenges ranging from gas separations to
catalysis, a lack of understanding of how to improve their stability limits
their use in practice. To overcome this limitation, we extract thousands of
published reports of the key aspects of MOF stability necessary for their
practical application: the ability to withstand high temperatures without
degrading and the capacity to be activated by removal of solvent molecules.
From nearly 4,000 manuscripts, we use natural language processing and automated
image analysis to obtain over 2,000 solvent-removal stability measures and
3,000 thermal degradation temperatures. We analyze the relationships between
stability properties and the chemical and geometric structures in this set to
identify limits of prior heuristics derived from smaller sets of MOFs. By
training predictive machine learning (ML, i.e., Gaussian process and artificial
neural network) models to encode the structure-property relationships with
graph- and pore-structure-based representations, we are able to make
predictions of stability orders of magnitude faster than conventional
physics-based modeling or experiment. Interpretation of important features in
ML models provides insights that we use to identify strategies to engineer
increased stability into typically unstable 3d-containing MOFs that are
frequently targeted for catalytic applications. We expect our approach to
accelerate the time to discovery of stable, practical MOF materials for a wide
range of applications.
