Structural, electronic, and vibrational properties of high-density amorphous silicon: A first-principles molecular-dynamics study

Relationship between diffraction peak, network topology, and amorphous-forming ability in silicon and silica

The network topology in disordered materials is an important structural descriptor for understanding the nature of disorder that is usually hidden in pairwise correlations. Here, we compare the covalent network topology of liquid and solidified silicon (Si) with that of silica (SiO2) on the basis of the analyses of the ring size and cavity distributions and tetrahedral order. We discover that the ring size distributions in amorphous (a)-Si are narrower and the cavity volume ratio is smaller than those in a-SiO2, which is a signature of poor amorphous-forming ability in a-Si. Moreover, a significant difference is found between the liquid topology of Si and that of SiO2. These topological features, which are reflected in diffraction patterns, explain why silica is an amorphous former, whereas it is impossible to prepare bulk a-Si. We conclude that the tetrahedral corner-sharing network of AX2, in which A is a fourfold cation and X is a twofold anion, as indicated by the first sharp diffraction peak, is an important motif for the amorphous-forming ability that can rule out a-Si as an amorphous former. This concept is consistent with the fact that an elemental material cannot form a bulk amorphous phase using melt quenching technique.

Time-dependent principal component analysis: A unified approach to high-dimensional data reduction using adiabatic dynamics

Systematic reduction of the dimensionality is highly demanded in making a comprehensive interpretation of experimental and simulation data. Principal component analysis (PCA) is a widely used technique for reducing the dimensionality of molecular dynamics (MD) trajectories, which assists our understanding of MD simulation data. Here, we propose an approach that incorporates time dependence in the PCA algorithm. In the standard PCA, the eigenvectors obtained by diagonalizing the covariance matrix are time independent. In contrast, they are functions of time in our new approach, and their time evolution is implemented in the framework of Car-Parrinello or Born-Oppenheimer type adiabatic dynamics. Thanks to the time dependence, each of the step-by-step structural changes or intermittent collective fluctuations is clearly identified, which are often keys to provoking a drastic structural transformation but are easily masked in the standard PCA. The time dependence also allows for reoptimization of the principal components (PCs) according to the structural development, which can be exploited for enhanced sampling in MD simulations. The present approach is applied to phase transitions of a water model and conformational changes of a coarse-grained protein model. In the former, collective dynamics associated with the dihedral-motion in the tetrahedral network structure is found to play a key role in crystallization. In the latter, various conformations of the protein model were successfully sampled by enhancing structural fluctuation along the periodically optimized PC. Both applications clearly demonstrate the virtue of the new approach, which we refer to as time-dependent PCA.

Polyamorphism in tetrahedral substances: Similarities between silicon and ice

Tetrahedral substances, such as silicon, water, germanium, and silica, share various unusual phase behaviors. Among them, the so-called polyamorphism, i.e., the existence of more than one amorphous form, has been intensively investigated in the last three decades. In this work, we study the metastable relations between amorphous states of silicon in a wide range of pressures, using Monte Carlo simulations. Our results indicate that the two amorphous forms of silicon at high pressures, the high density amorphous (HDA) and the very high density amorphous (VHDA), can be decompressed from high pressure (∼20 GPa) down to the tensile regime, where both convert into the same low density amorphous. Such behavior is also observed in ice. While at high pressure (∼20 GPa), HDA is less stable than VHDA, at the pressure of 10 GPa both forms exhibit similar stability. On the other hand, at much lower pressure (∼5 GPa), HDA and VHDA are no longer the most stable forms, and, upon isobaric annealing, an even less dense form of amorphous silicon emerges, the expanded high density amorphous, again in close similarity to what occurs in ice.

Reconstruction and electronic properties of silicon nanosheets as a function of thickness

We have shown, using density functional theory calculations, that the properties of Si nanosheets change as a function of thickness. While Si(111) oriented nanosheets that are 0.56 nm thick (2-layers) display a novel reconstruction, classified as Si(111)-2 × 2 on both surface layers (T. Morishita, M. J. S. Spencer, S. P. Russo, I. K. Snook and M. Mikami, Chem. Phys. Lett., 2011, 506, 221), nanosheets that are up to a thickness of 1.42 nm show the Si(111)-2 × 1 surface reconstruction, that is seen on the bulk Si(111) surface, on both sides of the nanosheet. For these thicker nanosheets, the relative orientation of the π-chain structure on each surface of the nanosheet can either be the same or different, resulting in unique electronic properties. When the orientation is the same, there is a widening of the band gap, indicating that the interaction between the surface π-chains is not present when they are oriented in different directions. The electronic properties of the nanosheets approach those of the bulk by 1.42 nm thick. The variation in structural and electronic properties of Si nanosheets with different thicknesses, as shown in this study, highlights the novelty of these materials and their significance for applications in electronic device technologies.