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2D layered metal-organic frameworks (MOFs) are a new class of multifunctional materials that can provide electrical conductivity on top of the conventional structural characteristics of MOFs, offering potential applications in electronics and optics. Here, for the first time, we employ Machine Learning (ML) techniques to predict the thermodynamic stability and electronic properties of layered electrically conductive (EC) MOFs, bypassing expensive ab initio calculations for the design and discovery of new materials. Proper feature engineering is a very important factor in utilizing ML models for such purposes. Here, we show that a combination of elemental features, using generic statistical reduction methods and crystal structure information curated from the recently introduced EC-MOF database, leads to a reasonable prediction of the thermodynamic and electronic properties of EC MOFs. We utilize these features in training a diverse range of ML classifiers and regressors. Evaluating the performance of these different models, we show that an ensemble learning approach in the form of stacking ML models can lead to higher accuracy and more reliability on the predictive power of ML to be employed in future MOF research. 

Most of the chemistry in nanoporous materials with small pore sizes and windows takes place on the outer surface, which is in direct contact with the substrate/solvent, rather than within the pores and channels. Here, we report the results of our comprehensive atomistic molecular dynamics (MD) simulations to decipher the interaction of water with a realistic finite ∼5.1 nm nanoparticle (NP) model of ZIF-8, with edges containing undercoordinated Zn metal sites, vs a conventionally employed pristine crystalline bulk (CB) model. The hydrophobic interior surface of the CB model imparts significant dynamical behavior on water molecules with (i) increasing diffusivity from the surface toward the center of the pores and (ii) confined water, at low concentration, showing similar diffusivity to that of the bulk water. On the other hand, water molecules adsorbed on the surface of the NP model exhibit a range of characteristics, including “coordinated”, “confined”, and “bulk-like” behavior. Some of the water molecules form coordinative bonds with the undercoordinated Zn metal centers and act as nucleation sites for the water droplets to form, facilitating diffusion into the pores. However, diffusion of water molecules is limited to the areas near the surface and not all the way to the core of the NP model. Our atomistic MD simulations provide insights into the stability of ZIFs in aqueous solutions despite hydrolysis of their outer surface. Such insights are helpful in designing more robust nanoporous materials for applications in humid environments.