Related papers: Complex $\mathrm{Ga}_{2}\mathrm{O}_{3}$ Polymorphs…
Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies,…
Ulta-wide bandgap semiconductors based on $\beta$-Ga$_2$O$_3$ offer the potential to achieve higher power switching performance, efficiency, and lower manufacturing cost than today's wide bandgap power semiconductors. However, the most…
Monoclinic $\beta$-Ga$_2$O$_3$, a promising wide band gap semiconducting material, exhibits complex, anisotropic diffusional characteristics and mass transport behavior as a results of its low symmetry crystal structure. From…
A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultra-fast force field (UF3) framework. The UF3 MLIP reproduces key properties in strong agreement with DFT including lattice constants, interlayer…
Hydrogenation of amorphous silicon (a-Si:H) is critical for reducing defect densities, passivating mid-gap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modelling the atomic scale structure…
Recently, van der Waals heterostructure has attracted interest both theoretically and experimentally for their potential applications in photoelectronic devices, photovoltaic devices, plasmonic devices and photocatalysis. Inspired by this,…
A Gaussian Approximation Potential (GAP) was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO2 facets through simulated annealing. Ab initio thermodynamics identifies (101) and…
The electrical and optical properties of semiconductor materials are profoundly influenced by the atomic configurations and concentrations of intrinsic defects. This influence is particularly significant in the case of $\beta$-$\rm…
We present a comprehensive investigation of reconstructions on $\beta$-Ga$_2$O$_3$(001) combining first-principles calculations with experimental observations. Using ab initio atomistic thermodynamics and replica-exchange grand-canonical…
Rapid design and development of the emergent ultra-wide bandgap semiconductors Ga$_2$O$_3$ and Al$_2$O$_3$ requires a compact model of their electronic structures, accurate over the broad energy range accessed in future high-field,…
We present an accurate interatomic potential for graphene, constructed using the Gaussian Approximation Potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT)…
Ultrawide bandgap semiconductor gallium oxide (Ga2O3) and its polymorphs have recently attracted increasing attention across physics, materials science, and electronics communities. In particular, the self-organized formation of the…
TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian Approximation Potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for…
Machine learning interatomic potentials (MLIPs) have massively changed the field of atomistic modeling. They enable the accuracy of density functional theory in large-scale simulations while being nearly as fast as classical interatomic…
We propose a long-range corrected hybrid meta-GGA functional, based on a global hybrid meta-GGA functional, M05 [Y. Zhao, N. E. Schultz, and D. G. Truhlar, J. Chem. Phys. 123, 161103 (2005)], and empirical atom-atom dispersion corrections.…
A potential orthorhombic phase of Ta$_2$O$_5$, designated as Y-Ta$_2$O$_5$, is predicted under high-pressure conditions through density functional theory (DFT) calculations combined with structural search algorithms. This phase, consisting…
Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are important for atomistic simulations of compositionally complex materials such as high-entropy alloys. Here, we study two state-of-the-art MLIP frameworks,…
In this work, we present a machine-learned interatomic potential for the ${\alpha}$-Fe-H system based on the tabulated Gaussian Approximation Potential (tabGAP) formalism. Trained on a Density Functional Theory (DFT) dataset of atomic…
We report that single interatomic potential, developed using Gaussian regression of density functional theory calculation data, has high accuracy and flexibility to describe phonon transport with ab initio accuracy in two different…
Although equivariant neural networks have become a cornerstone for learning electronic Hamiltonians, the intrinsic non-orthogonality of linear combinations of atomic orbitals (LCAO) basis sets poses a fundamental challenge. The…