With the development of technology in materials science, nanomaterials have emerged as a distinct class, showcasing unique properties at the nanoscale compared to bulk materials. Among these, one-dimensional tubular structures, exemplified by carbon nanotubes, exhibit fascinating and adjustable properties dictated by structural parameters. In parallel, metal-organic frameworks are a tunable material class with diverse applications. By simply relying on reticular chemistry, trillions of hypothetical MOFs can be generated, necessitating a screening process for practical applications. In this work with the goal to discover and investigate new one-dimensional porous materials, advanced ab initio techniques are employed, involving the rolling of a graphene-like structure and the customization of metal-covalent frameworks with one-dimensional topology. The results demonstrate promising properties, particularly in the realms of optoelectronics, thermoelectric performance and hydrogen storage capabilities. These findings hold potential applicability for machine learning models with the goal to predict the properties of novel structures in these specific domains.
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