Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H(2)O from N(2) and O(2) for...

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Detalles Bibliográficos
Autores principales: Li, Lifeng, Shi, Zenan, Liang, Hong, Liu, Jie, Qiao, Zhiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8746952/
https://www.ncbi.nlm.nih.gov/pubmed/35010109
http://dx.doi.org/10.3390/nano12010159

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8746952/
https://www.ncbi.nlm.nih.gov/pubmed/35010109
http://dx.doi.org/10.3390/nano12010159

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