Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.

Structural, mechanical, electronic and optical properties of biphenylene hydrogenation: a first-principles study.

Biphenylene networks typically exhibit a metallic electronic nature, while hydrogenation can open the band gap changing it to a semiconductor. This property makes hydrogenated biphenylene a promising candidate for use in semiconductor optoelectronic materials and devices. In this work, three representative configurations of hydrogenated biphenylene, denoted by α, ß and γ, were investigated. The structural, mechanical, electronic, and optical properties of these hydrogenated biphenylene configurations were calculated by first-principles calculations. Band gaps with HSE correction were 4.69, 4.42 and 4.39 eV for α, ß, and γ configurations, respectively. Among these three configurations, ß presents the best electronic performance and special elastic properties (negative Poisson's ratio), while γ exhibits the best elastic properties. In addition, we comprehensively analyze the mechanical properties of these configurations and provide evidence that hydrogenated biphenylene possibly exhibits a negative-Poisson's-ratio along the zigzag and armchair directions when hydrogen atoms are added to biphenylene in certain ways. Furthermore, although the electronic properties of γ are weaker than those of ß, they are also excellent. In addition, the binding energies of ß and γ are relatively lower, which indicates that ß and γ are more stable. Our findings demonstrate that the hydrogenated biphenylene is a promising material with significant application potential in optoelectronic devices.

The luminescence mechanism of ligand-induced interface states in silicon quantum dots.

Over decades of research on photoluminescence (PL) of silicon quantum dots (Si-QDs), extensive exploratory experiments have been conducted to find ways to improve the photoluminescence quantum yield. However, the complete physical picture of Si-QD luminescence is not yet clear and needs to be studied in depth. In this work, which considers the quantum size effect and surface effect, the optical properties of Si-QDs with different sizes and surface terminated ligands were calculated based on first principles calculations. The results show that there are significant differences in the emission wavelength and emission intensity of Si-QD interface states connected by different ligands, among which the emission of silicon-oxygen double bonds is the strongest. When the size of the Si-QD increases, the influence of the surface effect weakens, and only the silicon-oxygen double bonds still localize the charge near the ligand, maintaining a high-intensity luminescence. In addition, the presence of surface dangling bonds also affects luminescence. This study deepens the understanding of the photoluminescence mechanism of Si-QDs, and provides a direction for both future improvement of the photoluminescence quantum efficiency of silicon nanocrystals and for fabricating silicon-based photonic devices.

A new direct band gap Si-Ge allotrope with advanced electronic and optical properties.

Direct-band silicon materials have been a sought-after material for potential applications in silicon photonics and solar cells. Accordingly, methodologies like nanostructure engineering, alloy engineering and strain engineering have been developed. In this work, the particle swarm optimization (PSO) algorithm is used to design direct-band Si-Ge alloys. The findings of phonon computations demonstrate that all these structures are dynamically stable. In addition, ab initio molecular dynamics and elastic constant calculations are carried out, with results indicating these structures are thermodynamically stable at 300 K, as well as being mechanically stable. All of these materials exhibit semiconductor behavior with band gaps of 1.03, 0.68 and 1.37 eV for α, ß and γ phases, respectively, at the HSE06 level. The results of effective mass and mobility of carriers that are important in applications show that holes are more easily transported in all structures, with higher concentration of holes accompanied by lower carrier mobility. Different concentrations of holes nh lead to different limits in the scattering process. When nh is lower than the value of around 1016 cm-3, deformation potential scattering is dominant, while the ionized impurity scattering process limits overall mobility when nh is higher than such a value. Finally, the absorption spectra shows that both α and ß phases have isotropic optical properties in the X- and Y-directions while strong anisotropy can be seen in the Z-direction. However, the γ phase exhibits no notable isotropy. This investigation finds three direct-band and potentially CMOS compatible materials, a finding which will benefit the development of high efficiency emitters or solar cells.

Electronic and optical properties of hydrogen-terminated biphenylene nanoribbons: a first-principles study.

The electronic structures and optical properties of novel 2D biphenylene and hydrogen-terminated nanoribbons of different widths which are cut from a layer of biphenylene were explored via first-principles calculations. The findings of phonon computations demonstrate that such a biphenylene is dynamically stable and shows metallic properties. The crystal orbital Hamilton population analysis indicates that the tetra-ring local structure results in anisotropic mechanical properties. For 1D nanoribbons, their band gaps shrink, and a direct-indirect transition occurs in the band gap as the width increases, transforming the nanoribbon to endow them with metallic characteristics at a certain width. This is attributed to the weak coupling between the tetra-ring atoms, shrinking the direct band gap at the Y point in the Brillouin zone. Finally, the contribution of interband transitions to the dielectric function in 6-, 9-, and 12-armchair biphenylene nanoribbons (ABNRs) was identified. The lowest peak in the imaginary part of the dielectric function ε2 spectrum was mainly a contribution of a Γ-Γ transition. As the width of ABNR increases, the transitions in the x direction become stronger while the transition strength in the y direction is not significantly altered. This investigation extends the understanding of the electronic and optical properties of 2D biphenylene and 1D nanoribbons, which will benefit the practical applications of these materials in optoelectronics and electronics.

Neural network potential for Zr-Rh system by machine learning.

Zr-Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr-Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases inab initiomolecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.