Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Gas diffusion determines the performance of metal–organic frameworks (MOFs) in various practical applications, including membrane-based separations, yet its experimental measurement is challenging. We presented an efficient computational framework that integrates high-fidelity molecular dynamics (MD) simulations with machine learning (ML) to predict the diffusivities of CO 2, N 2, O 2, CH 4, and H 2 in >18,000 synthesized and hypothetical MOFs. ML models trained on MD data accurately predicted gas diffusivities of any given MOF within minutes using only easily accessible structural and guest-related properties. We provided an interactive, user-friendly web interface for predicting diffusivities of MOFs to facilitate material selection. Leveraging ML-predicted diffusivities, we evaluated membrane-based gas separation performances of all MOFs for seven industrially important separations: CO 2 /N 2, CO 2 /CH 4, N 2 /CH 4, H 2 /CO 2, H 2 /CH 4, H 2 /N 2, and O 2 /N 2 . The best MOF membranes offering high selectivity and permeability were identified and analyzed by using molecular fingerprinting to reveal the critical chemical properties for designing next-generation MOFs.
Source: ACS Materials Au
Institution: Koç University
Language: English
Subfield: Inorganic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,500
OpenAlex ID: W4416148685