Yanchao Wang

CALYPSO: A method for crystal structure prediction

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc.) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and the highly successful rate of this CALYPSO method [Y. Wang, J. Lv, L. Zhu, Y. Ma, Phys. Rev. B 82 (2010) 094116] [29]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works.

Particle-swarm structure prediction on clusters

We have developed an efficient method for cluster structure prediction based on the generalization of particle swarm optimization (PSO). A local version of PSO algorithm was implemented to utilize a fine exploration of potential energy surface for a given non-periodic system. We have specifically devised a technique of so-called bond characterization matrix (BCM) to allow the proper measure on the structural similarity. The BCM technique was then employed to eliminate similar structures and define the desirable local search spaces. We find that the introduction of point group symmetries into generation of cluster structures enables structural diversity and apparently avoids the generation of liquid-like (or disordered) clusters for large systems, thus considerably improving the structural search efficiency. We have incorporated Metropolis criterion into our method to further enhance the structural evolution towards low-energy regimes of potential energy surfaces. Our method has been extensively benchmarked on Lennard-Jones clusters with different sizes up to 150 atoms and applied into prediction of new structures of medium-sized Li(n) (n = 20, 40, 58) clusters. High search efficiency was achieved, demonstrating the reliability of the current methodology and its promise as a major method on cluster structure prediction.

High pressure partially ionic phase of water ice

Water ice dissociates into a superionic solid at high temperature (>2,000 K) and pressure, where oxygen forms the lattice, but hydrogen diffuses completely. At low temperature, however, the dissociation into an ionic ice of hydronium (H(3)O)(+) hydroxide (OH)(-) is not expected because of the extremely high energy cost (~1.5 eV) of proton transfer between H(2)O molecules. Here we show the pressure-induced formation of a partially ionic phase (monoclinic P2(1) structure) consisting of coupled alternate layers of (OH)(δ-) and (H(3)O)(δ+) (δ=0.62) in water ice predicted by particle-swarm optimization structural search at zero temperature and pressures of >14 Mbar. The occurrence of this ionic phase follows the break-up of the typical O-H covalently bonded tetrahedrons in the hydrogen symmetric atomic phases and is originated from the volume reduction favourable for a denser structure packing.

Predicting two-dimensional boron-carbon compounds by the global optimization method.

We adopt a global optimization method to predict two-dimensional (2D) nanostructures through the particle-swarm optimization (PSO) algorithm. By performing PSO simulations, we predict new stable structures of 2D boron-carbon (B-C) compounds for a wide range of boron concentrations. Our calculations show that: (1) All 2D B-C compounds are metallic except for BC(3) which is a magic case where the isolation of carbon six-membered ring by boron atoms results in a semi-conducting behavior. (2) For C-rich B-C compounds, the most stable 2D structures can be viewed as boron doped graphene structures, where boron atoms typically form 1D zigzag chains except for BC(3) in which boron atoms are uniformly distributed. (3) The most stable 2D structure of BC has alternative carbon and boron ribbons with strong in-between B-C bonds, which possesses a high thermal stability above 2000 K. (4) For B-rich 2D B-C compounds, there is a novel planar-tetracoordinate carbon motif with an approximate C(2)(v) symmetry.

An effective structure prediction method for layered materials based on 2D particle swarm optimization algorithm

A structure prediction method for layered materials based on two-dimensional (2D) particle swarm optimization algorithm is developed. The relaxation of atoms in the perpendicular direction within a given range is allowed. Additional techniques including structural similarity determination, symmetry constraint enforcement, and discretization of structure constructions based on space gridding are implemented and demonstrated to significantly improve the global structural search efficiency. Our method is successful in predicting the structures of known 2D materials, including single layer and multi-layer graphene, 2D boron nitride (BN) compounds, and some quasi-2D group 6 metals(VIB) chalcogenides. Furthermore, by use of this method, we predict a new family of mono-layered boron nitride structures with different chemical compositions. The first-principles electronic structure calculations reveal that the band gap of these N-rich BN systems can be tuned from 5.40 eV to 2.20 eV by adjusting the composition.

First-principles structural design of superhard materials

We reported a developed methodology to design superhard materials for given chemical systems under external conditions (here, pressure). The new approach is based on the CALYPSO algorithm and requires only the chemical compositions to predict the hardness vs. energy map, from which the energetically preferable superhard structures are readily accessible. In contrast to the traditional ground state structure prediction method where the total energy was solely used as the fitness function, here we adopted hardness as the fitness function in combination with the first-principles calculation to construct the hardness vs. energy map by seeking a proper balance between hardness and energy for a better mechanical description of given chemical systems. To allow a universal calculation on the hardness for the predicted structure, we have improved the earlier hardness model based on bond strength by applying the Laplacian matrix to account for the highly anisotropic and molecular systems. We benchmarked our approach in typical superhard systems, such as elemental carbon, binary B-N, and ternary B-C-N compounds. Nearly all the experimentally known and most of the earlier theoretical superhard structures have been successfully reproduced. The results suggested that our approach is reliable and can be widely applied into design of new superhard materials.

Self-assembled ultrathin nanotubes on diamond (100) surface

Surfaces of semiconductors are crucially important for electronics, especially when the devices are reduced to the nanoscale. However, surface structures are often elusive, impeding greatly the engineering of devices. Here we develop an efficient method that can automatically explore the surface structures using structure swarm intelligence. Its application to a simple diamond (100) surface reveals an unexpected surface reconstruction featuring self-assembled carbon nanotubes arrays. Such a surface is energetically competitive with the known dimer structure under normal conditions, but it becomes more favourable under a small compressive strain or at high temperatures. The intriguing covalent bonding between neighbouring tubes creates a unique feature of carrier kinetics (that is, one dimensionality of hole states, while two dimensionality of electron states) that could lead to novel design of superior electronics. Our findings highlight that the surface plays vital roles in the fabrication of nanodevices by being a functional part of them.

Spiral chain O4 form of dense oxygen

Oxygen is in many ways a unique element: It is the only known diatomic molecular magnet, and it exhibits an unusual O8 cluster in its high-pressure solid phase. Pressure-induced molecular dissociation as one of the fundamental problems in physical sciences has been reported from theoretical or experimental studies of diatomic solids H2, N2, F2, Cl2, Br2, and I2 but remains elusive for molecular oxygen. We report here the prediction of the dissociation of molecular oxygen into a polymeric spiral chain O4 structure (space group I41/acd, θ-O4) above 1.92-TPa pressure using the particle-swarm search method. The θ-O4 phase has a similar structure as the high-pressure phase III of sulfur. The molecular bonding in the insulating ε-O8 phase or the isostructural superconducting ζ-O8 phase remains remarkably stable over a large pressure range of 0.008–1.92 TPa. The pressure-induced softening of a transverse acoustic phonon mode at the zone boundary V point of O8 phase might be the ultimate driving force for the formation of θ-O4. Stabilization of θ-O4 turns oxygen from a superconductor into an insulator by opening a wide band gap (approximately 5.9 eV) that originates from the sp3-like hybridized orbitals of oxygen and the localization of valence electrons.

High-pressure pyrolysis study of C3N6H6: a route to preparing bulk C3N4

In order to prepare bulk C3N4, high-pressure pyrolysis of melamine (C3N6H6) at different temperatures was carried out. The products were characterized by C, N, H element analysis, Fourier transform infrared spectroscopy, x-ray photoelectron spectroscopy, and x-ray diffractometry. The results of the analysis reveal that graphitic phase C3N4 has been synthesized. It provides a novel route to synthesis of the theoretical superhard cubic C3N4 and other C3N4 phases from organic compounds by a high-pressure and high-temperature method.

Pressure-stabilized superconductive yttrium hydrides

The search for high-temperature superconductors has been focused on compounds containing a large fraction of hydrogen, such as SiH4(H2)2, CaH6 and KH6. Through a systematic investigation of yttrium hydrides at different hydrogen contents using an structure prediction method based on the particle swarm optimization algorithm, we have predicted two new yttrium hydrides (YH4 andYH6), which are stable above 110 GPa. Three types of hydrogen species with increased H contents were found, monatomic H in YH3, monatomic H+molecular “H2” in YH4 and hexagonal “H6” unit in YH6. Interestingly, H atoms in YH6 form sodalite-like cage sublattice with centered Y atom. Electron-phonon calculations revealed the superconductive potential of YH4 and YH6 with estimated transition temperatures (Tc) of 84–95 K and 251–264 K at 120 GPa, respectively. These values are higher than the predicted maximal Tc of 40 K in YH3.