Unifying CO2 diffusion mechanisms in diamine-functionalized metal-organic frameworks via quantum-accurate machine learning dynamics

초록

Diamine-functionalized Mg2(dobpdc) metal-organic frameworks (MOFs) (dobpdc4-= 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) exhibit exceptional CO2 capture, yet their CO2 diffusion behavior has remained difficult to rationalize across pore sizes, loadings, and functionalization. Here we resolve this gap by combining diffusion measurements with quantum-accurate machine-learning-potential molecular dynamics (MLP-MD) to establish a unified, loading-dependent mechanism for CO2 transport. We focus on bare and functionalized Mg frameworks featuring strong open-metal-site interactions and well-established cooperative CO2 adsorption chemistry. Our MLPs reproduce density functional theory energetics and forces, enabling converged diffusion coefficients and microscopic trajectories. By comparing Mg2(dobdc), Mg2(dobpdc), and Mg2(dotpdc), we show that CO2 is transiently trapped near open Mg sites at low uptake, producing suppressed diffusivity in both MLP-MD simulations and diffusion measurements. With increasing uptake, the progressive saturation of these strong adsorption sites screens their trapping potential, leading to a rise in diffusion coefficients for Mg2(dobpdc) and Mg2(dotpdc), quantitatively consistent with experiments. Diamine functionalization further accelerates CO2 transport by limiting access to open Mg sites and introducing cooperative CO2-diamine interactions. Finally, the same MLPs accurately predict CO2 adsorption isotherms via grand canonical Monte Carlo. Together, the experimentally validated MLP-MD framework reveals how pore size and diamine functionalization synergistically govern CO2 diffusion, providing design rules for high-capacity MOFs with improved mass transport.

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