Computational tools have been widely used for predicting material properties in the past. Nowadays, the rapid development of computers has increased interest in these tools due to their speed and low cost compared to physical experiments. Furthermore, mixing nanomaterials with other materials to create sols, emulsions, gels, or foams can lead to improved material properties. Efficient computational screening techniques are needed to quickly discover the most promising combinations. Atomistic simulations can be used for the screening process after digitally reconstructing the systems to be investigated. However, digitally reconstructing systems containing secondary phases, such as Nanoparticles (NPs), Nanosheets (NSs), and/or Nanotubes (NTs), is not a straightforward task. NanoConstruct (http://enaloscloud.novamechanics.com/diagonal/nanotube/), a toolbox powered by the Enalos Cloud Platform (enaloscloud.novamechanics.com), has been developed to overcome this barrier. NanoConstruct uses Crystallographic Information Files available on crystallographic databases as input to geometrically reconstruct crystalline NPs, while maintaining stoichiometry by removing excess atoms on the surface. Additionally, NanoConstruct searches the OPENKIM database and selects the Force-Field (FF) that is less generic and simultaneously contains every chemical element of the NP. The option to select a different OPENKIM FF than the suggested one is also available. After the FF selection, energy minimization is applied to investigate the NP's stability, while several descriptors are calculated for subsequent Machine Learning analysis. A similar procedure is applied for the NS and NT construction.