Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal-Organic Frameworks.

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal-organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO2. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal-Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

Anomaly in the Chain Length Dependence of n-Alkane Diffusion in ZIF-4 Metal-Organic Frameworks.

Molecular diffusion is commonly found to slow down with increasing molecular size. Deviations from this pattern occur in some host materials with pore sizes approaching the diameters of the guest molecules. A variety of theoretical models have been suggested to explain deviations from this pattern, but robust experimental data are scarcely available. Here, we present such data, obtained by monitoring the chain length dependence of the uptake of n-alkanes in the zeolitic imidazolate framework ZIF-4. A monotonic decrease in diffusivity from ethane to n-butane was observed, followed by an increase for n-pentane, and another decrease for n-hexane. This observation was confirmed by uptake measurements with n-butane/n-pentane mixtures, which yield faster uptake of n-pentane. Further evidence is provided by the observation of overshooting effects, i.e., by transient n-pentane concentrations exceeding the (eventually attained) equilibrium value. Accompanying grand canonical Monte Carlo simulations reveal, for the larger n-alkanes, significant differences between the adsorbed and gas phase molecular configurations, indicating strong confinement effects within ZIF-4, which, with increasing chain length, may be expected to give rise to configurational shifts facilitating molecular propagation at particular chain lengths.

Molecular Building Block-Based Electronic Charges for High-Throughput Screening of Metal-Organic Frameworks for Adsorption Applications.

Metal-organic frameworks (MOFs) are porous crystalline materials with attractive properties for gas separation and storage. Their remarkable tunability makes it possible to create millions of MOF variations but creates the need for fast material screening to identify promising structures. Computational high-throughput screening (HTS) is a possible solution, but its usefulness is tied to accurate predictions of MOF adsorption properties. Accurate adsorption simulations often require an accurate description of electrostatic interactions, which depend on the electronic charges of the MOF atoms. HTS-compatible methods to assign charges to MOF atoms need to accurately reproduce electrostatic potentials (ESPs) and be computationally affordable, but current methods present an unsatisfactory trade-off between computational cost and accuracy. We illustrate a method to assign charges to MOF atoms based on ab initio calculations on MOF molecular building blocks. A library of building blocks with built-in charges is thus created and used by an automated MOF construction code to create hundreds of MOFs with charges "inherited" from the constituent building blocks. The molecular building block-based (MBBB) charges are similar to REPEAT charges-which are charges that reproduce ESPs obtained from ab initio calculations on crystallographic unit cells of nanoporous crystals-and thus similar predictions of adsorption loadings, heats of adsorption, and Henry's constants are obtained with either method. The presented results indicate that the MBBB method to assign charges to MOF atoms is suitable for use in computational high-throughput screening of MOFs for applications that involve adsorption of molecules such as carbon dioxide.