  We envision an autonomous sorbent materials foundry (SMF) for rapidly
evaluating materials for direct air capture of carbon dioxide (CO2),
specifically targeting novel metal organic framework materials. Our proposed
SMF is hierarchical, simultaneously addressing the most critical gaps in the
inter-related space of sorbent material synthesis, processing, properties, and
performance. The ability to collect these critical data streams in an agile,
coordinated, and automated fashion will enable efficient end-to-end sorbent
materials design through machine learning driven research framework.
