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Register a new userEfficient method to calculate total energies of large nanoclusters
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We present an approach to calculate total energies of nanoclusters based on first principles estimates. For very large clusters the total energy can be separated into surface, edge and corner energies, in addition to bulk contributions. Using this separation and estimating these with direct, first principles calculations, together with the relevant chemical potentials, we have calculated the total energies of Cu and CdSe tetrahedrons containing a large number of atoms. In our work we consider polyhedral clusters so that in addition our work provides direct information on relaxation. For Cu the effects are very small and the clusters vary uniformly from very small to very large sizes. For CdSe there are important variations in surface and edge structures for specific sizes; nevertheless, the approach can be used to extrapolate to large non-stoichiometric clusters with polar surfaces.
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High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the zigzag and armchair directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies.
The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the elements potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated $beta$-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.
The exfoliation energy, the energy required to peel off an atomic layer from the surface of a bulk material, is of fundamental importance in the science and engineering of two-dimensional materials. Traditionally, the exfoliation energy of a material has been obtained from first principles by calculating the difference in the ground-state energy between (i) a slab of $N$ atomic layers ($N gg 1$) and (ii) a slab of $N-1$ atomic layers plus an atomic layer separated from the slab. In this paper, we prove that the exfoliation energy can be obtained exactly as the difference in the ground-state energy between a bulk material (per atomic layer) and a single isolated layer. The proposed method is (i) tremendously lower in computational cost than the traditional approach since it does not require calculations on thick slabs, (ii) still valid even if there is a surface reconstruction of any kind, (iii) capable of taking into account the relaxation of the single exfoliated layer (both in-plane lattice parameters and atomic positions), and (iv) easily combined with all kinds of many-body computational methods. As a proof of principles, we calculated exfoliation energies of graphene, hexagonal boron nitride, MoS$_2$ and phosphorene using density-functional theory. In addition, we found that the in-plane relaxation of an exfoliated layer accounts for 5% of one-layer exfoliation energy of phosphorene while it is negligible (< 0.4%) in the other cases.
We present a self-contained description of the wave-function matching (WFM) method to calculate electronic quantum transport properties of nanostructures using the Landauer-Buttiker approach. The method is based on a partition of the system between a central region (conductor) containing $N_S$ sites and an asymptotic region (leads) characterized by $N_P$ open channels. The two subsystems are linearly coupled and solved simultaneously using an efficient sparse linear solver. Invoking the sparsity of the Hamiltonian matrix representation of the central region, we show that the number of operations required by the WFM method in conductance calculations scales with $sim N_Stimes N_P$ for large $N_S$.
We present an efficient post-processing method for calculating the electronic structure of nanosystems based on the divide-and-conquer approach to density functional theory (DC-DFT), in which a system is divided into subsystems whose electronic structure is solved separately. In this post process, the Kohn-Sham Hamiltonian of the total system is easily derived from the orbitals and orbital energies of subsystems obtained by DC-DFT without time-consuming and redundant computation. The resultant orbitals spatially extended over the total system are described as linear combinations of the orbitals of the subsystems. The size of the Hamiltonian matrix can be much reduced from that for conventional calculation, so that our method is fast and applicable to general huge systems for investigating the nature of electronic states.
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