Articles tagged with Density Functional Theory
The article reviews electronic-structure methods for materials design, discussing their capabilities, limitations, and potential applications. The authors highlight the importance of combining simulations with experiments and emphasize the need for advanced computational infrastructure to support these efforts.
Scientists have developed a new method to reduce error in atomic force evaluation, expanding the scope of the quantum Monte Carlo framework. This breakthrough enables accurate prediction of atomic properties in materials, particularly those with unusual electronic and magnetic properties.
Researchers from University of Jyväskylä studied nuclear charge radii of exotic potassium isotopes using collinear resonance ionization spectroscopy. The results showed that the potassium isotope with a neutron number of 32 does not conform to magic neutron number criteria, challenging current understanding of nuclear forces.
A machine learning model permits full quantum description of the solvated electron, capturing its complex behavior and dynamics. The model revealed transient diffusion, a rare event not present in classical simulations.
Scientists used environmental transmission electron microscopy to visualize epitaxial rotation of gold nanoparticles on titanium dioxide surfaces during CO oxidation. Theoretical calculations showed that the epitaxial orientation could be induced by changing O2 adsorption coverage at the perimeter interface.
Researchers from the University of Münster and Düsseldorf provide an in-depth summary of the dynamical density functional theory, a method used to describe interacting particles. The article covers various branches of physics and applications in chemistry, solid state physics, and biophysics.
Researchers at USTC have designed a simple method to synthesize single crystalline wurtzite CZIS and CZGS nanobelts with exposed (0001) facets, showing excellent photocatalytic performances under visible-light irradiation. This work demonstrates the significance of surface engineering in quaternary sulfide photocatalysts.
Cyclohexyl phenyl sulfide cleavage studied for degradation of sulfur-containing heavy oil. Copper compounds are found to be the most effective catalysts for heavy oil extraction, followed by cobalt and nickel.
Researchers have designed efficient indium oxide catalysts for converting CO2 into methanol with high activity and selectivity. Theoretical modeling identified a specific facet of the catalyst as most favorable for methanol synthesis, which was confirmed by experimental results.
Researchers developed machine learning methods to predict nanoparticles' structures and atomic dynamics, significantly faster than traditional simulation methods. The new methods facilitated efficient explorations of particle-particle reactions and particles' functionality in their environment.
Scientists from Japan and Italy use synchrotron X-ray total scattering and vibrational spectroscopies to determine the structural disorder and dimensions of a building unit in the heterogeneous Ziegler-Natta catalyst. The research sheds new light on the full elucidation of nanostructure in practical heterogeneous catalysts.
The research aims to provide an effective and efficient simulation technology for designing high-performance gas sensors and sensor arrays. The team will model physisorption and chemisorption of gas molecules on sensor surfaces and study environmental effects.
A recent study found that the autocatalysis of water enhances the formation of COOH intermediate through proton transfer, accelerating CO generation while hindering methanol synthesis. The research also revealed that high initial partial pressure of water inhibits CO2 conversion due to excessive OH* coverage.
Researchers developed a new method using Machine Learning technique PCA to reduce dimensionality and facilitate the discovery of heterogeneous catalysts. The model allows for rapid and accurate survey of whole reaction networks on metallic surfaces, slashing DFT calculations needed.
The researchers developed site-dependent +U correction parameters using self-consistent first principles calculations, which improves the structure of stoichiometric SrMnO3 and provides a more accurate description of defective systems. This approach can lead to lower computational cost and more precise predictions of defect energetics.
Pedot is an organic polymer that has been the subject of much experimental research, but few theoretical studies have been conducted. Researchers at Linköping University have developed a new theory of electronic structure and optical properties of PEDOT, overturning previous research. This new understanding has significant implications...
The study of 'dancing' hyperons in pear-shaped hypernuclei reveals unique behaviors and new insights into fundamental interactions. Researchers found that the hyperon's spatial distribution has a spherical symmetry when occupying the lowest-energy state, shrinking nuclear size and decreasing quadrupole deformation.
Researchers use computational chemistry to explore interactions between organic molecules and surfaces, gaining insights into designing patterned surfaces for next-generation semiconductors. High-performance computing enables simulations of molecular dynamics, revealing new phenomena and improving the understanding of chemical reactions.
Researchers find that widely-used correction methods are based on a faulty assumption, potentially leading to inaccurate predictions. The team proposes new universal method for prediction that works for the right reasons.
The Atesins used supercomputers at the Texas Advanced Computing Center to study organometallic compounds and understand the structure of a palladium catalyst. Their research revealed that the most stable form of the molecule is chair-shaped, and repulsion between this conformation and the substrate dictates the final product.
Researchers developed a novel localized orbital scaling correction (LOSC) framework to eliminate delocalization errors in density functional theory. The LOSC function achieves size-consistent removal of errors, improving the accuracy of Kohn-Sham calculations, particularly for large systems.
Recent studies in density functional theory (DFT) have raised concerns about the accuracy of approximations used in computational chemistry. Researchers found that even with improved energy calculations, the quality of electron density simulations worsened over time. This contradiction highlights a fundamental flaw in DFT's approach.
Researchers used DFT to calculate electronic, elastic, and vibrational properties of BiAlO3. The study explores its crystal structure, space group R3c, and lattice parameter a = b = c = 5.338?A.
A study compared 35 basil cultivars and two hydroponic systems, finding that plant performance is more closely related to the choice of cultivar than the system used. Basil fresh weights varied significantly among cultivars, with some producing moderate-high yields.
The NSF-funded COMET program aims to train graduate STEM students to use DFT intelligently, bridging the gap between fundamental assumptions and numerical methods. Students will also receive professional development training and international internship opportunities.
Researchers at SLAC and Stanford have found a way to estimate uncertainties in computer calculations used to speed the search for new materials, improving confidence in discoveries. This technique can be applied to thousands of computational studies across various fields.
Physicists from India reveal Ti-V alloys' superconductivity is influenced by local magnetic fluctuations and spin fluctuations. The competition between these fluctuations and electron-phonon interaction determines the superconducting temperature threshold, contradicting previous assumptions.
Researchers demonstrate highest reported drive current on a transistor made of a monolayer of tungsten diselenide, achieving ON currents as high as 210 uA/um and mobility of 142 cm2/V.s. The discovery is significant for future low-power and high-performance integrated circuits.
Weinan E, a professor at Princeton University, will be awarded the Ralph E. Kleinman Prize for his exceptional work on stochastic partial differential equations and turbulence. He is recognized for his ability to bridge the gap between mathematics and applications, with an impact on various fields such as materials science and physics.
Recent progress enables interpretation and prediction of actinide compounds' properties using first principle theory. Researchers are identifying molecules that can extract uranium from its natural environment, improving nuclear energy systems and environmental remediation technologies.