Investigating Water Adsorption in Amine-Appended Metal-Organic Frameworks using Density Functional Theory-Derived Neural Network Potentials

CO₂ adsorption in polyamine-appended metal-organic frameworks (MOFs) exhibits a cooperative step-shaped isotherm [1], allowing for complete adsorption/desorption within small pressure/temperature swings. As a consequence, these materials are promising candidates for reversible carbon capture, particularly in humid conditions as the amine-appended group protects open metal sites from water poisoning. Nonetheless, the role of water molecules on the adsorption properties and mechanism in such systems is not clearly understood. In this context, ab initio neural network potentials (NNPs), derived from density functional theory calculations, are a promising tool to simulate atomistic dynamics for such large and complex systems at finite temperature. In this work, we investigate water effects on CO₂ adsorption in the MOF framework Mg₂(olz) (olz4- = (E)-5,5′-(diazene-1,2-diyl) bis(2-oxidobenzoate)) with a sample of appended diamine groups. Exploiting the computational efficiency and near-first-principles accuracy of our NNPs, we are able to identify water binding sites, discover lower energy structures, and ultimately predict water and CO₂ diffusion coefficients in these systems.

[1] McDonald, Thomas M., et al, J. Am. Chem. Soc. 2012, 134, 16, 7056–7065