Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, ΔLMFFL, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below ΔLMFFL = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the ΔLMFFL threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.