Machine learning highlights chemistry as the key factor in metal–organic frameworks for atmospheric water harvesting
Grand Canonical Monte Carlo simulations of diverse MOFs reveal key structure–function relationships. Machine learning identifies chemical features governing water uptake and atmospheric water harvesting performance.
Authors
- Nour Alkhatib (ORCID: https://orcid.org/0000-0003-0715-7230)
- Serdal Kırmızıaltın (ORCID: https://orcid.org/0000-0001-8380-5725)
- Saifeldeen Abed Alrhman
Institutions
- New York University Abu Dhabi (AE)
- New York University (US)
Publication Details
- Journal
- Molecular Systems Design & Engineering
- Published
- 2025-10-07
- DOI
- https://doi.org/10.1039/d5me00101c
- Primary Topic
- Metal-Organic Frameworks: Synthesis and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00