Related papers: Transferability of force fields for 2D silicon (si…
Interest in two dimensional materials has exploded in recent years. Not only are they studied due to their novel electronic properties, such as the emergent Dirac Fermion in graphene, but also as a new paradigm in which stacking layers of…
A multiscale simulation approach is developed to simulate the contact transport properties between semimetal to a monolayer two-dimensional (2D) transition metal dichalcogenide (TMDC) semiconductor. The results elucidate the mechanisms for…
Compared to electronic phase transitions, structural phase transitions of crystals are challenging to control due to the energy cost of breaking dense solid bonds. Recently, however, electric field switching of stacking configuration…
This study delves into the intricate electronic and optical behaviors of two-dimensional (2D) honeycomb materials, such as Stannen, arsenene, antimonene, silicene, and bismuthene(bismuth molecule), through the lens of first-principles…
In this article, we present a systematic study in developing machine learning force fields (MLFF) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training sets from molecular…
Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-property relations in the system. In this work, we fit an atomic…
Low-buckled silicene, germanene, and stanene are group$-IV$ graphene allotropes. They form a honeycomb lattice out of two interpenetrating ($A$ and $B$) triangular sublattices that are vertically separated by a small distance $\Delta_z$.…
The first principles density functional theory (DFT) calculations have been used to investigate the atomic and electronic properties of thiadiazole adsorption on the Si(001) surface. A (2x2) reconstructed clean substrate surface has been…
Machine-learned interatomic potentials (MLIPs) based on message passing neural networks hold promise to enable large-scale atomistic simulations of complex materials with ab initio accuracy. A number of MLIPs trained on energies and forces…
The interest in two-dimensional and layered materials continues to expand, driven by the compelling properties of individual atomic layers that can be stacked and/or twisted into synthetic heterostructures. The plethora of electronic…
The bandgap dependence on the number of atomic layers of some families of 2D-materials, can be exploited to engineer and use lateral heterostructures (LHs) as high-performance Field-Effect Transistors (FET). This option can provide very…
Electron doping is an excellent tuning knob to explore different phases of matter in two-dimensional (2D) materials. For example, tuning the Fermi level at a van Hove singularity in twisted bilayer graphene can enhance electron-electron…
Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their efficiency and ability to predict systems…
We have studied the {\em Zitterbewegung} effect on an infinite two dimensional sheet with honeycomb lattice. By tuning the perpendicular electric field and the magnetization of the sheet, it can enter different topological phases. We have…
We use a recently-developed machine-learned Moment Tensor Potential (MTP) trained on data generated with the density functional theory (DFT) and tailored to amorphous silicon coupled with the Activation-Relaxation Technique nouveau (ARTn)…
Diamondynes are a recently synthesized three-dimensional carbon allotrope, with interlocked and movable sublattices that introduce deformation modes not present in standard architected materials. Here, we report the first multiscale…
Carbon's versatility allows it to form diverse structures with unique properties, driven by its moderate electronegativity, small ionic radius, and ability to adopt \textit{sp}, \textit{sp\textsuperscript{2}}, and…
Stepped well-ordered semiconductor surfaces are important as nanotemplates for the fabrication of one-dimensional nanostructures which are candidates of intriguing electronic properties. Therefore a detailed understanding of the underlying…
Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work,…
Principles of design to create dynamically stable transition metal, lanthanide, and actinide based low-dimensional borides are presented. A charge transfer analysis of donor metal atoms to electron deficient honeycombed B lattices allows to…