Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning

Yangzesheng Sun, Robert F. DeJaco, Zhao Li, Dai Tang, Stephan Glante, David S. Sholl, Coray M. Colina, Randall Q. Snurr, Matthias Thommes, Martin Hartmann, J. Ilja Siepmann

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

Original languageEnglish (US)
Article numbereabg3983
JournalScience Advances
Issue number30
StatePublished - Jul 2021

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