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.
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CCS Contributor(s)
Software Contributions Supported by CCS award
Portions of the development of this software were supported by CCS award.
Summary
an open‐source meta-learning model for the prediction of unary adsorption for nanoporous materials based on example adsorption data for a material