  We report a workflow and the output of a natural language processing
(NLP)-based procedure to mine the extant metal-organic framework (MOF)
literature describing structurally characterized MOFs and their solvent removal
and thermal stabilities. We obtain over 2,000 solvent removal stability
measures from text mining and 3,000 thermal decomposition temperatures from
thermogravimetric analysis data. We assess the validity of our NLP methods and
the accuracy of our extracted data by comparing to a hand-labeled subset.
Machine learning (ML, i.e. artificial neural network) models trained on this
data using graph- and pore-geometry-based representations enable prediction of
stability on new MOFs with quantified uncertainty. Our web interface,
MOFSimplify, provides users access to our curated data and enables them to
harness that data for predictions on new MOFs. MOFSimplify also encourages
community feedback on existing data and on ML model predictions for
community-based active learning for improved MOF stability models.

Materials Science;Machine Learning;Chemical Physics