SorbMetaML

SorbMetaML is an open‐source meta-learning model for the prediction of unary adsorption for nanoporous materials based on example adsorption data for a material. SorbMetaML has been used to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference for diverse nanoporous materials. Datasets for the hydrogen adsorption of all-silica zeolites, hyper-cross-linked polymers, and metal-organic frameworks are provided.