Work

Diffusion explorer for the COF space: Data-driven discovery of high-performing COF membranes for gas separations

Year: 2025

Type: article

Abstract: Covalent organic frameworks (COFs) have recently emerged as highly promising candidates for membrane-based gas separations, exhibiting superior performance relative to conventional membrane materials. Nevertheless, the rapidly expanding number of COFs renders the experimental evaluation of each material’s membrane performance infeasible. In this study, we investigated the COF space comprising approximately 70,000 synthesized and hypothetical materials using high-throughput molecular dynamics (MD) simulations and machine learning (ML) for computing the diffusivities of CO 2 , CH 4 , H 2 , N 2 , and O 2 gases. We generated an online toolbox by integrating our ML models to estimate gas diffusivities of any given COF material in seconds. Using the ML-predicted diffusivities, gas permeabilities and selectivities of COF membranes were assessed for seven industrially relevant separations; CO 2 /CH 4 , CO 2 /N 2 , H 2 /CO 2 , H 2 /N 2 , H 2 /CH 4 , O 2 /N 2 , N 2 /CH 4 . The performance of COF membranes was compared to traditional membrane materials, and the most promising COFs were identified and analyzed using molecular fingerprinting to reveal the critical structural and chemical features for accelerating the design of next-generation COF membranes.

Source: Carbon Capture Science & Technology

DOI: 10.1016/j.ccst.2025.100559

Authors: Gokhan Onder Aksu, Seda Keskin

Institution: Koç University

Language: English

Topic: Membrane Separation and Gas Transport

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,000

Funders: European Commission, HORIZON EUROPE Framework Programme, European Research Council