Prediction of Metal-Organic Framework Adsorption Isotherms using Ab Initio derived Neural Network Potentials

Metal-organic frameworks (MOFs) form an extensive class of porous materials of interest for new applications, and therefore, predicting their properties computationally is crucial in advancing the exploration of this wide chemical space. A predictive and generalizable method for calculations of isotherms could accelerate the discovery of new promising MOFs for gas adsorption applications. While empirical force fields can be combined with grand canonical Monte Carlo (GCMC) simulations to estimate adsorption isotherms in specific MOFs [1], this approach relies on fixed functional forms as well as an extensive system-specific parameterization, limiting its applicability to a broader set of MOFs or to automated force field generation. Ab initio neural network potentials (NNPs), derived from density functional theory calculations, are a promising and generalizable alternative for accurate binding energetics and isotherm prediction. In this work, we combine ab initio NNPs and GCMC simulations to predict CO2 adsorption isotherms for well-studied MOF, Mg2(dobpdc) (dobpdc4- = 4,4’-dioxidobiphenyl-3,3’-dicarboxylate), comparing to prior simulations and experimentals.

[1] Mercado et al., J. Phys. Chem. C 2016, 120, 23, 12590–12604