Role of molecular modelling in the development of metal-organic framework for gas adsorption applications

Impact of ligand fields on Kubas interaction of open copper sites in MOFs with hydrogen molecules: an electronic structural insight

We investigate hydrogen sorption on open copper sites in various ligand coordinations of metal-organic frameworks (MOFs), including the triangular T(CuL3) in MFU-4l, the linear L(CuL2) in NU2100, and the paddlewheel P(CuL4)2 in HKUST-1 from an electronic structure perspective using DFT calculations. The ligand-field-induced splitting of d states and spd hybridizations in copper are thoroughly examined. The hybridization between Cu s, p, and d orbitals occurs in various forms to optimize the Coulomb repulsion of different ligand fields. Despite the Cu+ oxidation state, which is typically conducive to strong Kubas interactions with hydrogen molecules, the vacant spd z 2 hybrid orbitals of the open copper site in the L(CuL2) coordination are unsuitable for facilitating electron forward donation, thereby preventing effective hydrogen adsorption. In contrast, the vacant spd z 2 hybrid orbitals in the T(CuL3) and P(CuL4)2 coordinations can engage in electron forward donations, forming bonding states between the Cu spd z 2 and H2 σ bonding orbitals. The forward donation in the T(CuL3) configuration is significantly stronger than in the P(CuL4)2 configuration due to both the lower energy of the vacant orbitals and the larger contributions of p and d z 2 characters to the hybrid orbital. Additionally, the occupied Cu pd xz/yz and pd x 2-y 2 hybrid orbitals in the T(CuL3) configuration promote electron back donation to the H2 σ* antibonding orbital, leading to the formation of π bonding states. In the P(CuL4)2 coordination, the repulsion from the electron density distributed over the surrounding ligands prevents the H2 molecule from approaching the copper center closely enough for the back donation to occur. The complete Kubas interaction, involving both forward and back electron donations, results in a large dihydrogen-copper binding energy of 37.6 kJ mol-1 in the T(CuL3) coordination. In contrast, the binding energy of 10.6 kJ mol-1 in the P(CuL4)2 coordination is primarily driven by electrostatic interactions with a minor contribution of the Kubas-like forward donation interaction. This analysis highlights the pivotal role of coordination environments in determining the hydrogen sorption properties of MOFs.

Insights into hydrogen and methane storage capacities: Grand canonical Monte Carlo simulations of SIGSUA

In the pursuit of sustainable energy solutions, the development of materials with efficient hydrogen and methane storage capacities is imperative, particularly for advancing hydrogen-powered vehicles. Metal-organic frameworks (MOFs) have emerged as promising candidates to meet the stringent targets set by the Department of Energy for both hydrogen and methane storage. This study employs Grand Canonical Monte Carlo simulations to investigate the usable hydrogen and methane gravimetric and volumetric storage capacities of the recently synthesized SIGSUA. A comparative analysis encompasses the selected MOFs with similar metal compositions, those with comparable density and average pore radius, and classical benchmarks, such as IRMOF-15 and IRMOF-20, all evaluated at room temperature and moderate pressures ranging from 25 to 35 MPa. The results reveal that SIGSUA demonstrates noteworthy gravimetric and volumetric storage capacities for both hydrogen and methane, rivaling or surpassing those of the selected MOFs for analysis. These findings underscore the potential of SIGSUA in advancing clean energy storage technologies.

Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning

In recent data-driven approaches to material discovery, scenarios where target quantities are expensive to compute and measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose. It is applied to predict the band gap and adsorption properties of metal-organic frameworks (MOFs), a novel class of materials that results from the virtually infinite combinations of their building units. Simpler and low dimensional descriptors, such as those based on stoichiometric and geometric properties, are used to compute the feature space for this model owing to their ability to better represent MOFs in the low data regime. The partitions given by a regression tree constructed on the labeled part of the data set are used to select new samples to be added to the training set, thereby limiting its size while maximizing the prediction quality. Tests on the QMOF, hMOF, and dMOF data sets reveal that our method constructs small training data sets to learn regression models that predict the target properties more efficiently than existing active learning approaches, and with lower variance. Specifically, our active learning approach is highly beneficial when labels are unevenly distributed in the descriptor space and when the label distribution is imbalanced, which is often the case for real world data. The regions defined by the tree help in revealing patterns in the data, thereby offering a unique tool to efficiently analyze complex structure-property relationships in materials and accelerate materials discovery.