ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Июнь 5, 2025

Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer poor stability in water. In addition to water stability, high uptake capacity ambient conditions is expected be necessary water-related practical of MOFs, motivating large-scale search can only achieved computationally. Here, we take a combined machine learning and high-throughput screening approach identify water-stable with capacities. Starting subset previously curated experimentally known exceptionally water, explore the effect linker functionalization 12 diverse hydrophilic functional groups further tune uptake. For these 736 use grand canonical Monte Carlo (GCMC) simulations compute their capacity. We observe strong positive correlations between MOF pore features (e.g., largest cavity diameter volumetric volume) capacity, although notice breakdowns extremely hydrophobic linkers repel molecules despite having large pores. Finally, develop models screen new simultaneously From pool hypothetical experimental 74 within domain applicability predicted both have

Язык: Английский