Related papers: SiC-TGAP: A machine learning interatomic potential…
Silicon Carbide (SiC) is a wide bandgap semiconductor material recently being used in replacement of traditional semiconductors for high-voltage power device applications. Radiation environments induce defects through displacement damage in…
High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's…
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning…
We present a tight-binding potential for transition metals, carbon, and transition metal carbides, which has been optimized through a systematic fitting procedure. A minimal basis, including the s, p electrons of carbon and the d electrons…
Silicon detectors play a crucial role in high energy physics experiments. In future high energy physics experiments, silicon detectors will be exposed to extremely high fluence environment, which can significantly affect their performance.…
We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total…
Silicon carbide is a promising material for radiation-hard detectors due to its wide bandgap, low leakage current, high critical electric field, and high saturation velocity. A key obstacle for its use in high-radiation environments is the…
Radiation damage in structural materials is a major challenge for advanced nuclear energy systems, and niobium is of particular interest due to its high melting point, mechanical strength, and corrosion resistance. To better understand its…
We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential approach, we present LC-GAP, a new system for generating…
An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However,…
Two-dimensional (2D) silicon carbide is an emergent direct band-gap semiconductor, recently synthesized, with potential applications in electronic devices and optoelectronics. Here, we study nuclear quantum effects in this 2D material by…
Silicon carbide has excellent physical properties, such as high stability, thermal conductivity, and mechanical strength. It has been widely used in high-power devices, catalysis, material processing, and other fields and is of great…
Thermal stability of silicene and thin silicon films is studied by molecular dynamics using two machine-learning potentials, SNAP and GAP. For SNAP potential, systems ranging from a single silicene layer to films of 36 layers are…
SiC/SiC composite tubes are studied as materials for nuclear fuel cladding. A thorough understanding on the mechanisms of damage to this material requires both an experimental and a numerical study. In situ tensile tests were performed…
Silicon Carbide (SiC) demonstrates significant potential for high-energy particle detection in complex radiation environments due to its exceptional radiation resistance, excellent environmental adaptability, and fast response time.…
Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning based interatomic potentials have shown sufficiently high…
Neutral silicon-carbon divacancy (V$_{Si}$V$_{C}$) in cubic silicon carbide (3C-SiC) is a promising class of point defects for quantum technologies based on active crystalline centers. Within the theoretical framework of spin-polarized…
We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide…
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…
Localized basis ab initio molecular dynamics simulation within the density functional framework has been used to generate realistic configurations of amorphous silicon carbide (a-SiC). Our approach consists of constructing a set of smart…