Pyrene-based MOFs have several applications; including luminescence, photocatalysis, adsorption and separation, heterogeneous catalysis, electrochemical applications and bio-medical applications.

Metal-organic frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic-organic crystalline materials offer an ideal platform to incorporate light-harvesting and catalytic centers and thus, exhibit a great potential to be exploited in solar-driven photocatalytic processes such as H 2 production and CO 2 reduction. To be photocatalytically active, UV-visible optical absorption and appropriate band alignment with respect to the target redox potential is required. Despite fulfilling these criteria, the photocatalytic performance of MOFs is still limited by their ability to produce long-lived electron-hole pairs and long-range charge transport. Here, a computational strategy is presented to address these two descriptors in MOFs and to translate them into charge transfer numbers and effective mass values. The approach is applied to 15 MOFs from the literature that encompass the main strategies used in the design of efficient photocatalysts including different metals, ligands, and topologies. The results capture the main characteristics previously reported for these MOFs and enable to identify promising candidates. In the quest of novel photocatalytic systems, high-throughput screening based on charge separation and charge mobility features are envisioned to be applied in large databases of both experimentally and in silico generated MOFs.

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The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material.However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the heat requirement is reduced by as much as a factor of two using the correct heat capacity.