相关论文: The Tungsten Project: Dielectronic Recombination D…
Transition Radiation (TR) plays an important role in particle identification in high-energy physics and its characteristics provide a feasible method of energy calibration in the energy range up to 10 TeV, which is of interest for dark…
Hydrogen as a fuel plays a crucial role in driving the transition to net zero greenhouse gas emissions. To realise its potential, obtaining a means of efficient storage is paramount. One solution is using metal hydrides, owing to their good…
State-of-the-art NLTE model-atmosphere codes have arrived at a high level of numerical sophistication and are now useful tools to analyze high-quality spectra from the infrared to the X-ray wavelength range. The capacity of current…
The combination of deep learning and ab initio materials calculations is emerging as a trending frontier of materials science research, with deep-learning density functional theory (DFT) electronic structure being particularly promising. In…
A practical method for finding free energy barriers for transitions in high-dimensional classical and quantum systems is presented and used to calculate the dissociative sticking probability of H2 on a metal surface within transition state…
The dinuclear system model incorporates several essential input physical quantities, including nuclear mass, fission barrier, shell correction energy, level density parameter, and shell damping factor, etc., which are derived from diverse…
Data driven materials discovery and optimization requires databases that are error free and experimentally verified. Performing material measurements are time-consuming and often restricted by the fact that material sample preparations are…
Tungsten is considered as one of the most promising materials for nuclear fusion reactor chamber applications. Wire + Arc Additive Manufacturing has already demonstrated the ability to deposit defect-free large-scale tungsten structures,…
Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one…
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered,…
The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously embraced across several distinct efforts existing at three…
Density Functional Tight-Binding (DFTB), an approximative approach derived from Density Functional Theory (DFT), has the potential to pave the way for simulations of large periodic or non-periodic systems. We have specifically tailored DFTB…
A possible design rule for preventing surface damage from thermal transients to solid tungsten armour is proposed and formulated for the plasma facing components (divertor, first wall) of magnetic fusion machines. The rule is based on…
The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of elastic and defect properties of the pure material in order to…
This revisit gives a survey on the analytical methods for the inverse exponential Radon transform which has been investigated in the past three decades from both mathematical interests and medical applications such as nuclear medicine…
The main purpose of this work is to perform an analysis of realistic new trajectories for a robotic mission to Saturn's largest moon, Titan, in order to demonstrate the great advantages related to the Direct Fusion Drive (DFD). The DFD is a…
Precision physics at future multi-TeV lepton colliders such as CLIC requires excellent jet energy resolution. The detectors need deep calorimeter systems to limit the energy leakage also for very highly energetic particles and jets. At the…
The integration of machine learning techniques with triboelectric nanogenerators (TENGs) offers a transformative pathway for optimizing energy harvesting technologies. In this study, we propose a comprehensive framework that utilizes graph…
Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear…
Reliable predictions of nuclear properties are needed as much to answer fundamental science questions as in applications such as reactor physics or data evaluation. Nuclear density functional theory is currently the only microscopic, global…