Understanding the effect of density functional choice and van der Waals treatment on predicting the binding configuration, loading, and stability of amine-grafted metal-organic frameworks

Metal organic frameworks (MOFs) are crystalline, 3-dimensional structures with high surface areas and tunable porosities. Made from metal nodes connected by organic linkers, the exact properties of a given MOF are determined by node and linker choice. MOFs hold promise for numerous applications, including gas capture and storage. M$_2$(4,4'-dioxidobiphenyl-3,3'-dicarboxylate) - henceforth simply M$_2$(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn - is regarded as one of the most promising structures for CO$_2$ capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO$_2$ concentrations in the direct-air capture (DAC) of CO$_2$. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO$_2$ adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO$_2$ adsorption capacity and kinetics, and so a validation of computational prediction of these quantities is required to suitably use computational methods in the discovery and screening of amine-functionalized sorbents. In this work, we study the predictive accuracy of density functional theory (DFT) and related calculations on amine loading and configuration for one diamine and two tetramines. In particular, we explore the Perdew-Burke-Ernzerhof (PBE) functional and its formulation for solids (PBEsol) with and without the Grimme-D2 and Grimme-D3 pairwise corrections (PBE+D2/3 and PBEsol+D2/3), two revised PBE functionals with the Grimme-D2 and Grimme-D3 pairwise corrections (RPBE+D2/3 and revPBE+D2/3), and the non-local van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning inter-atomic potential's (M3GNet) predictive accuracy for loading and configuration. These results allow us to identify a useful screening procedure for configuration prediction that has a coarse component for quick evaluation and a higher accuracy component for detailed analysis. Our general observation is that the NNP can be used as a high-level and rapid screening tool, whereas PBEsol+GD3 gives a completely qualitatively predictive picture across all systems studied, and can thus be used for high accuracy motif predictions. We close by briefly exploring the predictions of relative thermal stability for the different functionals and dispersion corrections.