(d) The first-layer-projected band along with the layer-projected DOSs of the In-vacancy β(2 × 2) in comparison with bulk InAs (ZB, dotted line). (c) The first-layer-projected band of the In-vacancy α(2 × 2). The set of blue arrows on a unit cell of the In-vacancy α(2 × 2) corresponds to the lowest imaginary mode at point. The dotted parallelograms of the (2 × 2) reconstructions correspond to the supercell used for DFT force calculations, which comprises four unit cells (solid parallelogram). (b) Top view of the topmost bilayer (first and second layers) of (111)B as-cleaved(1 × 1), In-vacancy α(2 × 2), and In-vacancy β(2 × 2). The bottom (111)A surface is passivated by hydrogens and the top three layers of the (111)B surface were selected for phonon evaluation. (a) Side view of the as-cleaved slab along the direction of InAs. All of the functionality is fully automated and the program execution can be managed through high-level user interfaces without difficulty.Īdditional comments: At the time of writing, the latest version of InterPhon is 1.3.0, in which the conjunction with VASP, Quantum ESPRESSO, and FHI-aims is supported. The strategy is efficiently implemented in a Python library capable of calculation setup, evaluation, analysis, and visualization for arbitrary interfacial systems in conjunction with any 3D DFT code. By limiting the range of phonon calculations to user-defined interfacial region, the enormous computational cost is mitigated. Solution method: Although the main obstacles are unavoidable, distinct interfacial phonons are confined to the vicinity of the interface. The problems are intrinsically inevitable within a three-dimensional (3D) DFT framework representing interfacial systems by supercells. However, there has been a limitation in applying ab initio phonon calculations to interfaces due to the excessive computational cost, introduced by their large number of atoms and broken symmetry. In particular, interfacial phonons play the key roles to unveil the largely unexplored atomic dynamics within the localized region, and this information is essential to make a prediction regarding the dependence of interface structures on process conditions. Nature of problem: The interface possesses diverse atomic structures and lattice vibrations, which are distinct from the bulk. Supplementary material: a PDF file descrbing details of phonon formalism using FDM, validation of symmetry functionality, InterPhon package architecture, method to define the interfacial region and convergence test, and calculation details for all of the results in this work. High-level automation in InterPhon will be of great help in elucidating interfacial atomic dynamics and in implementing an automated computational workflow for diverse interfacial systems. It leads to a prediction regarding the structural transition of interfaces and unveils the processing conditions for spontaneous growth of GaAs nanowires on Si. The third example, on a Si/GaAs interface, shows distinct vibrational patterns depending on interfacial structures. The second example, involving oxygen adsorption on Cu, reveals adsorption-induced vibrational change and its contribution to energetic stability. It eventually explains the anisotropic surface vibrations of the polar crystal. The first example, in which this package was applied to InAs surfaces, demonstrates a systematic structure search for unexplored surface reconstructions, navigated by the imaginary mode of surface phonons. InterPhon makes it possible to apply all of the phonon-based predictions that have been available for bulk systems, to interfacial systems. Its strategy of arbitrarily defining the interfacial region and periodicity alleviates the excessive computational cost in applying ab initio phonon calculations to interfaces and enables efficient extraction of interfacial phonons. This work provides the community with an easily executable open-source Python package designed to automize the evaluation of Interfacial Phonons (InterPhon).
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