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

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 zeol...

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Detalles Bibliográficos
Autores principales: Sun, Yangzesheng, DeJaco, Robert F., Li, Zhao, Tang, Dai, Glante, Stephan, Sholl, David S., Colina, Coray M., Snurr, Randall Q., Thommes, Matthias, Hartmann, Martin, Siepmann, J. Ilja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2021
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294760/
https://www.ncbi.nlm.nih.gov/pubmed/34290094
http://dx.doi.org/10.1126/sciadv.abg3983

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294760/
https://www.ncbi.nlm.nih.gov/pubmed/34290094
http://dx.doi.org/10.1126/sciadv.abg3983

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