Origin of non-linear piezoelectricity in III-V semiconductors: Internal strain and bond ionicity from hybrid-functional density functional theory

We derive first- and second-order piezoelectric coefficients for the zinc-blende III-V semiconductors, {Al,Ga,In}-{N,P,As,Sb}. The results are obtained within the Heyd-Scuseria-Ernzerhof hybrid-functional approach in the framework of density functional theory and the Berry-phase theory of electric polarization. To achieve a meaningful interpretation of the results, we build an intuitive phenomenological model based on the description of internal strain and the dynamics of the electronic charge centers. We discuss in detail first- and second-order internal strain effects, together with strain-induced changes in ionicity. This analysis reveals that the relatively large importance in the III-Vs of non-linear piezoelectric effects compared to the linear ones arises because of a delicate balance between the ionic polarization contribution due to internal strain relaxation effects, and the contribution due to the electronic charge redistribution induced by macroscopic and internal strain

Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C

Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory (DFT) and other quantum mechanics-based computational simulation techniques have been successful at delivering a detailed understanding of the atomic and electronic structure of crystalline semiconductors. Unfortunately, the complex structure of disordered semiconductors sets the time and length scales required for DFT simulation of these materials out of reach. In recent years, machine learning (ML) approaches to atomistic modeling have been developed that provide an accurate approximation of the DFT potential energy surface for a small fraction of the computational time. These ML approaches have now reached maturity and are starting to deliver the first conclusive insights into some of the missing details surrounding the intricate atomic structure of disordered semiconductors. In this Topical Review we give a brief introduction to ML atomistic modeling and its application to amorphous semiconductors. We then take a look at how ML simulations have been used to improve our current understanding of the atomic structure of a-C and a-Si

Searching for iron nanoparticles with a general-purpose Gaussian approximation potential

We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse thermodynamic conditions, as well as surfaces and nanoparticles (NPs). By studying its phase diagram, we show that our GAP remains stable at extreme conditions, including those found in the Earth's core. The new GAP is particularly accurate for the description of NPs. We use it to identify new low-energy NPs, whose stability is verified by performing density functional theory calculations on the GAP structures. Many of these NPs are lower in energy than those previously available in the literature up to $N_\text{atoms}=100$. We further extend the convex hull of available stable structures to $N_\text{atoms}=200$. For these NPs, we study characteristic surface atomic motifs using data clustering and low-dimensional embedding techniques. With a few exceptions, e.g., at magic numbers $N_\text{atoms}=59$, $65$, $76$ and $78$, we find that iron tends to form irregularly shaped NPs without a dominant surface character or characteristic atomic motif, and no reminiscence of crystalline features. We hypothesize that the observed disorder stems from an intricate balance and competition between the stable bulk motif formation, with bcc structure, and the stable surface motif formation, with fcc structure. We expect these results to improve our understanding of the fundamental properties and structure of low-dimensional forms of iron, and to facilitate future work in the field of iron-based catalysis

Prediction of strong ground state electron and hole wave function spatial overlap in nonpolar GaN/AlN quantum dots

We present a detailed analysis of the electrostatic built-in field, the electronic structure, and the optical properties of a-plane GaN/AlN quantum dots with an arrowhead-shaped geometry. This geometry is based on extensive experimental analysis given in the literature. Our results indicate that the spatial overlap of electron and hole ground state wave functions is significantly increased, compared to that of a c-plane system, when taking the experimentally suggested trapezoid-shaped dot base into account. This finding is in agreement with experimental data on the optical properties of a-plane GaN/AlN quantum dots. (C) 2012 American Institute of Physics. (http://dx.doi.org/10.1063/1.4752108

Impact of cation-based localized electronic states on the conduction and valence band structure of Al1-xInxN alloys

We demonstrate that cation-related localized states strongly perturb the band structure of Al1-xInxN leading to a strong band gap bowing at low In content. Our first-principles calculations show that In-related localized states are formed both in the conduction and the valence band in Al1-xInxN for low In composition, x, and that these localized states dominate the evolution of the band structure with increasing x. Therefore, the commonly used assumption of a single composition-independent bowing parameter breaks down when describing the evolution both of the conduction and of the valence band edge in Al1-xInxN. (C) 2014 AIP Publishing LLC