Related papers: Using neural network potential to study point defe…
We determine atomic and electronic structure, formation energy, stability and magnetic properties of native point defects, such as Gallium (Ga) and Nitrogen (N) vacancies in bulk and at non-polar (10$\overline{1}$0) surface of wurtzite…
Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transport with $ab$ $initio$ accuracy in pristine and defect-laden…
The semiconducting behaviour and optoelectronic response of gallium nitride is governed by point defect processes, which, despite many years of research, remain poorly understood. The key difficulty in the description of the dominant…
We study the formation energies of native point defects in GaN through density-functional theory. In our first-principles scheme, the band edges are positioned in accord with hybrid density functional calculations, thus yielding a band-gap…
Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks…
A precise analysis of point defects in solids requires accurate molecular dynamics (MD) simulations of large-scale systems. However, ab initio MD simulations based on density functional theory (DFT) incur high computational cost, while…
The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the…
As a representative of wide-bandgap semiconductors, wurtzite gallium nitride (GaN) has been widely utilized in high-power devices due to high breakdown voltage and low specific on resistance. Accurate prediction of wurtzite GaN thermal…
In this paper, we propose a combined modeling of molecular mechanics (MM) and the tight-binding (TB) approach, which enables us to study the effect of factors such as external local forces, constraints, and vacancy defects on electronic…
Calculations of formation energies and charge transition levels of defects routinely rely on density functional theory (DFT) for describing the electronic structure. Since bulk band gaps of semiconductors and insulators are not well…
Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus…
Power consumption is a major obstacle in the deployment of deep neural networks (DNNs) on end devices. Existing approaches for reducing power consumption rely on quite general principles, including avoidance of multiplication operations and…
Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time simulations of electrostatics-driven phenomena such as…
We present design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechanical/molecular-mechanical (QM/MM)…
We report calculated, electronic and related properties of wurtzite and zinc blende gallium nitrides (w-GaN, zb-GaN). We employed a local density approximation (LDA) potential and the linear combination of atomic orbital (LCAO) formalism.…
An oversight of several previous results from local density approximation (LDA) calculations appear to have led to an incomplete, and hence misleading, characterization of the capability of density functional theory (DFT) to describe…
A significant hurdle in developing high-performance semiconductor quantum technologies utilizing deep defects is related to charge dynamics. Unfortunately, progress in modeling their charge dynamics has been hindered over recent decades due…
Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical…
The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour…
Scaling wide-band-gap semiconductors to the ultrathin limit offers a transformative pathway for power electronics, with gallium nitride (GaN) representing a cornerstone material in this class. However, the operational resilience and…