We develop and apply novel computational models that enable predictive calculations of the structural, physical and chemical properties of nano-materials which constitute promising candidates for renewable energy production or storage. To tackle challenging nanoscale phenomena, i.e., in presence of significant quantum delocalization effects, many-body correlations, and employing accurate, first-principle descriptions of intermolecular interactions, we adopt an approach based on liquid density-functional theory. We focus here on the high-throughput computational screening and optimization of the nanostructures for specific applications, e.g., their capacities for hydrogen storage and for hydrogen production via photocatalytic water splitting, and the separation of hydrogen and helium isotopes. We compute the storage capacity and the isotope separation efficiency of nanomaterials (particularly, metal-organic and covalent-organic frameworks) with potential application in the design of efficient hydrogen storage or quantum sieving devices, respectively, in the whole range of pressures and temperatures of technological interest. Furthermore, we investigate the water splitting on cluster systems mimicking active sites on anode surfaces, and assess the influence of the modification of the topology of the surface, the presence of localized charges, chemical doping, etcetera, on the performance of electrodes for the oxygen evolution reaction. We aim to gain a deeper understanding of the microscopic mechanisms underlying the efficiency of the target applications, thereby paving the way to conceive more efficient devices of molecular size.

Density functional modeling of renewable energy production and storage at the nanoscale

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ACS Fall 2022

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