相关论文: Exploring magnetized liner inertial fusion with a …
Plasma supports collective modes and particle-wave interactions that leads to complex behavior in inertial fusion energy applications. While plasma can sometimes be modeled as a charged fluid, a kinetic description is useful towards the…
Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed-phase systems remains computationally demanding. Here we…
Using a time-dependent approach the analysis and optimization of a planar FEL-amplifier with an axial magnetic field and an irregular waveguide is performed. By applying methods of nonlinear dynamics three-dimensional equations of motion…
Ab initio simulations are capable of providing detailed information of material behavior at the nanoscale. Simulating experimentally relevant situations is, however, often computationally intense. Using hybrid approaches between ab initio…
Recent implosion experiments performed at the OMEGA laser facility reported by Casey et al.[1], displayed an anomalously low dd proton yield and a high tt neutron yield as compared to dt fusion reactions, explained as a stratification of…
Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searches of stable local minimum energy…
Accurate subgrid-scale turbulence models are needed to perform realistic numerical magnetohydrodynamic (MHD) simulations of the subsurface flows of the Sun. To perform large-eddy simulations (LES) of turbulent MHD flows, three unknown terms…
Heusler alloys are widely studied due to their interesting structural and magnetic properties, like magnetic memory shape ability, coupled magneto-structural phase transitions and half-metallicity; ruled, for many cases, by the valence…
We extend the program potfit, which generates effective atomic interaction potentials from ab initio data, to electrostatic interactions and induced dipoles. The potential parametrization algorithm uses the Wolf direct, pairwise summation…
Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (OF-DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate…
(Abridged): We study the collision of magnetized irregularities (shells) in relativistic outflows in order to explain the origin of the generic phenomenology observed in the non-thermal emission of both blazars and gamma-ray bursts. We…
Most numerical investigations on the role of magnetic fields in turbulent molecular clouds (MCs) are based on ideal magneto-hydrodynamics (MHD). However, MCs are weakly ionized, so that the time scale required for the magnetic field to…
The declared aim of Marvel Fusion is the realization of a reactor based on the aneutronic fusion of proton and boron-11 nuclei in the near future, making use of latest-day laser technology and nano-structured materials. The aim of the…
This work presents a more detailed analysis of the process of magnetic reconnection as promising ion beam accelerator mechanism with possible applications in laboratory plasmas and, more importantly, in the plasma propulsion field. In a…
The magnetic phase diagram of the Mn-based semi-Heusler alloys is determined at T=0 using first-principles calculations in conjunction with the frozen-magnon approximation. We show that the magnetism in these systems strongly depends on the…
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,…
The ballistic spin-filter effect from a ferromagnetic metal into a semiconductor has theoretically been studied with an intention of detecting the spin polarizability of density of states in FM layer at a higher energy level. The physical…
We present an ion kinetic model describing the ignition and burn of the deuterium-tritium fuel of inertial fusion targets. The analysis of the underlying physical model enables us to develop efficient numerical methods to simulate the…
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT).…
We describe an experiment to form and characterize a section of a spherically imploding plasma liner by merging six supersonic plasma jets that are launched by newly designed contoured-gap coaxial plasma guns. This experiment is a prelude…