Check out our first application of pore graph in ML. Beyond improved data efficiency, PoroNet provides intrinsic pore-level interpretability without expensive pore-level training labels, enabling scalable mechanistic insights and actionable design rules.
pubs.acs.org/doi/full/10....
pubs.acs.org
PoroNet: An Intrinsically Interpretable Pore Graph Neural Network for Resolving Pore-Level Adsorption in Metal–Organic Frameworks
Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal–organic frameworks (MOFs) for gas storage, chemical separations, and catalysis applications. T...
