Neural Network Potentials for CO2 Adsorption in Amine-Appended Metal-Organic Frameworks

Metal-organic frameworks (MOFs), a class of porous materials consisting of metal ions and organic ligands, are promising candidates for CO2 capture and separations applications. Amine-appended variants of MOFs Mg₂(dobpdc) (dobpdc^4- = 4,4’-dioxidobiphenyl-3,3’-dicarboxylate) selectively bind CO2 via a novel cooperative adsorption mechanism that gives rise to step-shaped isotherms, allowing for high working capacities to be attained with small temperature-swings[1]. The equilibrium thermodynamics of CO2 is quantified experimentally with the differential enthalpy which can be compared to zero temperature density functional theory (DFT) calculations. However, finite temperature studies within DFT are hindered by their computational complexity due to the large number of atoms per primitive cell, making the energetics and details of the CO2 insertion/deinsertion dynamics not easily accessible. Here, we summarize the development of an accurate and transferable neural network potential for amine-appended MOFs via active learning approach to enable a detailed study of the energetics and dynamical nature of CO2 bound amine-appended MOFs at finite temperatures.

[1] Kim et al., Science 369, 392–396 (2020)