Articles tagged with Density Functional Theory
Researchers have observed a new microscopic mechanism enabling precise control of magneto-optical properties in alloys of two-dimensional semiconductors. The discovery opens up prospects for technological applications in devices exploiting valleytronics.
Researchers developed a machine learning-based workflow, SPaDe-CSP, to predict crystal structures of organic molecules. The workflow narrows the search space by predicting probable space groups and crystal densities before computationally intensive relaxation steps.
University of Michigan researchers have made significant progress in developing a more accurate simulation approach for density functional theory, a widely used method in fundamental chemistry and materials science studies. The new approach has improved the calculation of exchange-correlation functionals, which describe how electrons i...
Researchers developed a universal approach to calculate liquid entropy using fundamental physical principles, achieving remarkable consistency with existing data. The new method predicts entropy accurately for various liquids, including sodium, and has significant implications for optimizing chemical reactions and material properties.
Scientists developed an algorithm that can accurately simulate atomic interactions on material surfaces, reducing the need for massive computing power. This breakthrough enables the analysis of complex chemical processes in just two percent of unique configurations, paving the way for improved battery performance.
Researchers developed a machine learning framework that can predict how materials respond to electric fields up to a million atoms, accelerating simulations beyond quantum mechanical methods. This allows for accurate, large-scale simulations of material responses to various external stimuli.
A study combines DFT and machine learning to analyze a wide range of epoxides in CO₂ cycloaddition, identifying key molecular descriptors and predicting reactivity trends. The research aims to develop predictive catalyst and substrate design for optimized CO₂ fixation, contributing to greener chemical processes.
Kobe University researchers uncover a new phenomenon in bismuth that masks its surface conductivity, relevant to topological materials suitable for quantum computing and spintronics. The study breaks the principle of bulk-edge correspondence, suggesting 'topological blocking' in other systems.
Researchers developed an in-situ EPR setup to accurately identify radicals generated by PAA activation under different UV wavelengths, revealing distinct radical generation pathways. The study provides new insights into the mechanisms of radical formation and transformation using density functional theory calculations.
Researchers developed two silver-based bimetallic clusters that increase Faradaic efficiency and yield of urea through charge polarization modulation. Ag14Pd outperforms Ag13Au5 in NO3RR, while Ag13Au5 excels in CO2RR with higher urea formation rates.
Researchers introduce a trimetallic catalyst supported on defective ceria, achieving extraordinary efficiency in CO2 reduction. The unique metal-support interaction fine-tunes the electronic structure, enabling optimal performance and setting new benchmarks in catalysis.
Researchers developed an automated analytical method to analyze single atom catalysts, which could lead to more efficient fuel production and sustainable energy. The new tool, called MS-QuantEXAFS, automates the analysis process, reducing time from days to months.
Researchers observed the breaking of carbon nanotube fibers due to molecular slippage, which reduces their strength. Electron irradiation enhances CNT bundles' strength by forming stronger bonds between molecules.
Researchers developed a novel AI approach to predict atomic-level chemical bonding information in 3D space, bypassing traditional supercomputer simulations. This methodology accelerates calculations by learning chemical bonding information using neural network algorithms from computer vision.
Researchers at the University of Münster have developed a new method for synthesizing heteroatom-substituted 3D molecules, which are more stable than related flat rings. The innovative structures show promise as substitutes in drug molecules, offering new possibilities for drug development.
A new atomically-thin material has been discovered that can switch between an insulating and conducting state by controlling the number of electrons. This property makes it a promising candidate for use in electronic devices such as transistors.
A team of scientists at Tokyo University of Science has discovered a novel substituent migration reaction that enables the creation of complex benzofurans. This breakthrough synthesis method uses alkynyl sulfoxide and trifluoroacetic anhydride to produce highly functionalized benzofurans with high yields.
Researchers used density functional theory to identify possible europium compounds as a new quantum memory platform. They synthesized one of the predicted compounds, Cs2NaEuF6, which is an air-stable material that could be used in scalable quantum computing.
Researchers have discovered dynamic piezoelectricity in ferroelectric hafnia, which can be changed by electric field cycling. This phenomenon offers new options for microelectronics and information technology. The study also suggests the possibility of an intrinsic non-piezoelectric ferroelectric compound.
Physicists at the University of Southampton successfully detect weak gravitational pull on microscopic particles using a new technique. The experiment, published in Science Advances, could pave the way to finding the elusive quantum gravity theory.
Researchers at BESSY II used RIXS and DFT simulations to analyze the electronic structures of fumarate, maleate, and succinate dianions. The study found that maleate is potentially less stable than fumarate and succinate due to its delocalized HOMO orbital, which can lead to weaker binding with molecules or ions.
Researchers have discovered that magnetostriction causes a magnetic phase transition in manganese oxide at 118K, leading to the switch of muon sites. The study uses advanced simulations and resolves a long-standing puzzle, shedding new light on antiferromagnetic oxides.
Researchers from Osaka University developed an economical catalyst for a common chemical transformation, replacing rare metals with cheaper substitutes like nickel. The novel catalyst showed high activity, reusability, and high yields.
Researchers from Osaka University have developed an operationally simple way to synthesize the intricate beta-lactam scaffold characteristic of beta-lactam antibiotics. The new catalytic system generates Fischer-carbene complexes in small quantities, eliminating toxic chromium waste and requiring only a small amount of catalyst.
Researchers identified a new theoretical framework for oscillating superconductivity, which could revolutionize electricity transfer. The discovery provides insight into an unconventional, high-temperature superconductive state seen in certain materials.
A new machine learning-based simulation method called Materials Learning Algorithms (MALA) has been developed, enabling accurate electronic structure calculations at large scales. MALA achieves this by utilizing a hybrid approach that combines physics-based approaches with machine learning to predict the electronic structure of materials.
Researchers employ DFT and NEST analysis to investigate pyrrolidinyl gold(I) complexes, revealing enhanced understanding of electronic and steric effects. The findings facilitate the design of novel chiral ligands for enantioselective reactions.
Researchers have directly observed the signatures of electron orbitals in two different transition-metal atoms, iron and cobalt, using atomic force microscopy. The study validated that the observed experimental differences primarily stem from the different electronic configurations in 3d electrons near the Fermi level.
A Rensselaer researcher has used artificial intelligence to discover novel van der Waals (vdW) magnets with large magnetic moments. These two-dimensional vdW magnets have the potential to advance science and technology in data storage, spintronics, and quantum computing.
Researchers developed GAME-Net, a graph neural network that rapidly evaluates adsorption energy for large molecules like plastics and biomass. The model achieves accuracy comparable to density functional theory (DFT) while utilizing simple molecular representations.
Researchers used a machine learning model to simulate the behavior of hydrogen atoms at high pressures, discovering a new phase that was missed by previous theories and experiments. The discovery has sparked further investigation into the properties of solid hydrogen under extreme conditions.
Scientists from the University of Groningen have developed a theoretical framework to explain how charges move through organic solar cells. The study provides insights into the ultrafast charge transfer process, which is crucial for improving the material's efficiency.
Researchers used density functional theory to investigate the mechanical properties of superionic ice XVIII, which is thought to make up a large part of Neptune and Uranus. The study found that dislocations in the crystal lattice produce shear, leading to macroscopic deformations and potentially influencing the planets' magnetic fields.
Researchers have discovered that nanodiamonds can emit solvated electrons in water when exposed to visible light, a crucial step towards using them as photocatalysts. This discovery could lead to the development of inexpensive and metal-free processes for converting CO2 into valuable hydrocarbons or converting N2 into ammonia.
Researchers at the University of Rochester used x-ray spectroscopy to study radiation transport in dense plasmas. They found that atomic energy level changes do not follow conventional quantum mechanics theories, instead conforming to a self-consistent approach based on density-functional theory.
Researchers at Monash University found that electric fields and applied strain can turn magnetism on and off in two-dimensional metal-organic frameworks. This discovery could lead to applications in magnetic memory, spintronics, and quantum computing.
A team of researchers from Japan Advanced Institute of Science and Technology developed an analytical tool to investigate the ordering of fluorine in lead titanium oxyfluoride. They used first-principles calculation to analyze experimental results and determined the element substitution positions, finding that fluorine atoms predominan...
Despite DeepMind's neural network claiming superiority, scientists question its performance on predicting electron interactions in chemical systems. The BBB test set shows limited understanding of fractional-electron systems, raising concerns about the AI's ability to generalize.
Brazilian researchers used computer simulations to investigate the superconducting behavior of a dimolybdenum nitride monolayer, finding that it became superconductive at relatively high temperatures and showed strong correlation with strain applied.
A team of scientists from Lawrence Berkeley National Laboratory has designed a new material system to overcome the challenges of mixed-plastic recycling. They created customized polydiketoenamine (PDK) plastics that can be recycled efficiently and indefinitely, providing a low-carbon manufacturing solution for plastic products.
Using the Stampede2 supercomputer, researchers have developed a deep learning model that predicts the properties of over 370,000 high-entropy alloy compositions. The study also applied association rule mining to discover design rules for high-entropy alloy development and proposed several compositions for experimentalists to synthesize.
Physicists at HZDR and CASUS improved the density functional theory method to accurately describe quantum many-body systems, breaking a significant simplification. This enables studies of non-linear phenomena in complex materials with unprecedented temporal and spatial resolution.
Researchers have developed an AI-powered approach to calculate molecular spectra using Graph Neural Networks (GNNs), significantly reducing computation time and improving accuracy. The SchNet model achieved a 20% increase in accuracy while reducing computational time, enabling the analysis of complex molecules like quantum dots.
A new machine-learning framework has been developed to improve the design of catalysts, which speed up chemical reactions. The approach analyzes the conversion of carbon monoxide to methanol using a copper-based catalyst and identifies key steps that need to be tweaked to increase productivity.
AV3Sb5 kagome metals exhibit unusual quantum phenomena such as high-temperature superconductivity. Researchers identified four Van Hove singularities near the Fermi level, which enhance correlation effects and lead to competing orders.
Researchers have developed an eco-friendly and reusable solution for removing toxic synthetic dyes from wastewater using nanocomposite-based hydrogels. The new material, made from carboxymethyl cellulose (CMC) and graphene oxide, demonstrates high adsorption capacities and retains its effectiveness even after multiple cycles of use.
Researchers developed an AI-powered model to assess rare-earth compound stability, leveraging machine learning and high-throughput density-functional theory. This framework has far-reaching applications in materials science, including designing new compounds for clean energy technologies and optimizing magnetic properties.
Researchers identify two key principles determining reaction specificity in converting CO2 and ethane into synthesis gas or ethylene. The formation energy of the bimetallic catalyst and binding energy between the catalyst and oxygen released from CO2 are crucial in driving reaction selectivity.
A Japanese research team successfully estimated the bending energy of disiloxane molecules with state-of-the-art quantum Monte Carlo method, overcoming previous simulation challenges. The method's self-healing property reduced basis-set dependence and bias, enabling accurate results without dependence on parameter choices.
Researchers at Kazan Federal University study hydroxyapatite's properties as a catalyst, finding that iron incorporation is energetically comparable and preferentially localized. The study uses density functional theory calculations to analyze the introduction of iron ions in the HAp lattice.
Scientists fabricate 1D and 2D boron sulfide (BS) nanosheets with unique electronic properties that can be controlled by changing the number of layers. The bandgap energy decreases as more layers are added, making BS a potential n-type semiconductor material.
Researchers from Shibaura Institute of Technology synthesized atropisomeric N-aryl quinazoline-4-thiones, showing unprecedented isotopic atropisomerism due to rotational restriction around an N-Ar bond. The findings support the formation of diastereomers and have potential applications in pharmaceuticals.
MnBi2Te4's unique properties make it suitable for ultra-low-energy electronics and observing exotic topological phenomena. The material is metallic along its one-dimensional edges while electrically insulating in its interior.
Researchers have developed a common workflow interface for various quantum codes, enabling accurate predictions of system properties and promoting the wider use of density-functional theory. The interface allows users to optimize structures using any code without defining parameters, providing reusable results.
Scientists at Tohoku University have developed a new mathematical model to predict the properties of carbon-based materials. The Standard Realization with Repulsive Interaction (SRRI) model abstracts key effects and reveals relationships between changes and resulting properties.
Scientists successfully synthesized cyclo[9]pyrroles via oxidative coupling of terpyrrole, showing intense absorption at 1,740 nm. The molecular structure and electronic properties were analyzed using NMR and X-ray diffraction, providing insights into the optical and physical properties of porphyrinoids.
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.