This presentation highlights how integrating high-throughput simulations and data science techniques can enhance our mechanistic understanding of MOF-based technologies.
Metal–organic frameworks (MOFs) and covalent-organic frameworks (COFs) offer unprecedented structural and chemical tunability, making them excellent platforms for gas sensing and separation. However, optimizing their properties requires navigating vast chemical search spaces and understanding complex multiscale phenomena. This presentation highlights how integrating high-throughput simulations and data science techniques can enhance our mechanistic understanding of MOF-based technologies.
First, we address the challenge of non-invasive disease diagnosis via human breath analysis.
(Nurhuda et al. Adv. Theory Simul. 2025, 8, 2401404)
Second, we explore computational workflows for predicting gas adsorption metrics across large structural databases.
(Nurhuda et al. Adv. Theory Simul. 2026, 9, e00003)
Finally, we consider composite materials by examining gas separation and gas transport in MOF-polymer mixed-matrix membranes (MMMs).
(Meza et al. Manuscript in preparation)
See more details at the above abstract
