Nookiin: Python software to build commensurable multilayer heterostructures
Description
Many first-principles packages employ periodic and symmetry conditions to reduce the computational time and cost. The supercell (SC) method is useful to address periodic systems with different physical perturbations; however, the theoretical definition of a specific SC is a real challenge in Crystallography and Solid State Physics studies. In particular, whether the system is commensurable and made of several two-dimensional (2D) layers with different Bravais lattice, initial local stacking, and interlayer relative orientation. This work presents Nookiin (from the junction of Yucatec Maya words, Nook: ’knit’ or ’wave’; and iin: ’me’), an open-source Python code, designed for the efficient generation of commensurable SCs using geometric methods. Nookiin has an efficient algorithm that minimizes structural distortions at a geometric level, providing an optimized approach for representing 2D heterostructures with a reduced number of atoms. Its modular architecture facilitates adaptation to different problems. Its use through both an interactive console interface and programmatic implementation allows seamless integration into scientific workflows. Additionally, Nookiin offers tools for structural visualization and export of configurations compatible with first-principles codes such as the Vienna ab initio Simulation Package (VASP) code [17]. This report presents the theoretical foundations of the method, the computational implementation of the algorithm, and the results obtained that validate its effectiveness in generating commensurable SCs. With these characteristics, Nookiin establishes itself as a versatile and alternative resource for research in Solid State Physics and Materials Science. The software is openly available at github.com/OssielAg/Nook-iin, with a citable release archived at doi.org/10.5281/zenodo.15706528.