ABSTRACT
Metal organic frameworks (MOFs) are crystalline, three-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. M2(4,4'-dioxidobiphenyl-3,3'-dicarboxylate)-henceforth simply M2(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn-is regarded as one of the most promising structures for CO2 capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO2 concentrations in the direct-air capture of CO2. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO2 adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO2 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 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 nonlocal van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning interatomic 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 neural network-based potential can be used as a high-level and rapid screening tool, whereas PBEsol+D3 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.
ABSTRACT
Refractory high-entropy alloys (RHEAs) show promising applications at high temperatures. However, achieving high strengths at elevated temperatures above 1173K is still challenging due to heat softening. Using intrinsic material characteristics as the alloy-design principles, a single-phase body-centered-cubic (BCC) CrMoNbV RHEA with high-temperature strengths (beyond 1000 MPa at 1273 K) is designed, superior to other reported RHEAs as well as conventional superalloys. The origin of the high-temperature strength is revealed by in situ neutron scattering, transmission-electron microscopy, and first-principles calculations. The CrMoNbV's elevated-temperature strength retention up to 1273 K arises from its large atomic-size and elastic-modulus mismatches, the insensitive temperature dependence of elastic constants, and the dominance of non-screw character dislocations caused by the strong solute pinning, which makes the solid-solution strengthening pronounced. The alloy-design principles and the insights in this study pave the way to design RHEAs with outstanding high-temperature strength.
