Surface Electronic Properties-Driven Electrocatalytic Nitrogen Reduction on Metal-Conjugated Porphyrin 2D-MOFs.

Two-dimensional (2D) metal organic framework (MOF) or metalloporphyrin nanosheets with a stable metal-N4 complex unit present the metal as a single-atom catalyst dispersed in the 2D porphyrin framework. First-principles calculations on the 3d-transition metals in M-TCPP are investigated in this study for their surface-dependent electronic properties including work function and d-band center. Crystal orbital Hamiltonian population (-pCOHP) analysis highlights a higher contribution of the bonding state in the M-N bond and antibonding state in the N-N bond to be essential for N-N bond activation. A linear relationship between ΔGmax and surface electronic properties, N-N bond strength, and Bader charge has been found to influence the rate-determining potential for nitrogen reduction reaction (NRR) in M-TCPP MOFs. 2D Ti-TCPP MOF, with a kinetic energy barrier of 1.43 eV in the final protonation step of enzymatic NRR, shows exclusive NRR selectivity over competing hydrogen reduction (HER) and nitrogenous compounds (NO and NO2). Thus, Ti-TCPP MOF with an NRR limiting potential of -0.35 V in water solvent is proposed as an attractive candidate for electrocatalytic NRR.

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors.

Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO2 capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO2 adsorption, with an R2 of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks' partial charges based on the expected CO2 uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO2 capture using pseudo-classification predictions.

Prediction of O2/N2 Selectivity in Metal-Organic Frameworks via High-Throughput Computational Screening and Machine Learning.

Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O2 and N2 uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O2/N2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r2 correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O2/N2 separation based on the adsorption and diffusion selectivity.