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Results for “DENSITY OF STATES”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Pattern formation in charge density wave states after a quantum quench
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Charge density wave state in extremely overdoped cuprates driven by phonons
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Densities of states and the Cohen-Kaplan-Nelson bound
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Universal formula for the density of states with continuous symmetry
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Flow-based density of states for complex actions
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Superconducting density of states and band structure at the surface of the candidate topological superconductor Au 2 Pb
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Impact of disorder in the charge density wave state of Pd-intercalated ErTe 3 revealed by the electrodynamic response
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Vacancy-Induced Low-Energy Density of States in the Kitaev Spin Liquid
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A machine learning surrogate for density functional theory based on the local density of state.
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Partial densities of states from x-ray absorption and Auger electron spectroscopy: Use of core-hole memory
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Ultrafast enhancement of electron-phonon coupling via dynamic quantum well states
Abstract The density of states at the Fermi surface controls many material properties by influencing the low-energy interactions of conductive electrons. Typically, external tuning knobs generate only small perturbations to the density of states in crystals, since their band structure depends strongly on the lattice potential. In contrast, quantum well states are contingent on a localized potential extrinsic to the crystal lattice and can be easily modified leading to changes in the density of states at the Fermi level. Here, we are able to control the quantum well potential on the surface of Bi 2 Se 3 with light, driving a density of states singularity below E F at ultrafast timescales, thereby triggering a reversible Lifshitz transition. We reveal a substantial ultrafast enhancement of the electron-phonon coupling with repercussions on the relaxation dynamics in the system. We argue that the relaxation dynamics are governed largely by the interplay of the dynamic density of states of the quantum wells with c -axis scattering from the $${A}_{{{{{{{{\rm{1g}}}}}}}}}^{2}$$ A 1g 2 optical phonon mode. These results demonstrate a powerful way to enhance electron-boson interaction on ultrafast timescales providing new avenues for controlling material properties and driving novel quantum phases of matter.
Attention-based quantum tomography
Abstract With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS ) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.
Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.
Trends in spatial correlations, two-phase coexistence, and criticality in a class of type-2 Schloegl models for autocatalysis
A class of type-2 Schloegl models is considered for particles on a square lattice with variable-range cooperativity. These models involve: (i) spontaneous particle annihilation at rate p; (ii) autocatalytic particle creation at unoccupied sites (i, j) with n ⩾ 2 particles within a specified neighborhood, Ω 𝑁 (i, j), of sites at rate $k_n$ = $\frac{^{(^n_2)}}{_{(^N_2)}}$ = $\frac{n{(n-1)}}{_{N(N-1)}}$; and (iii) possible spontaneous particle creation at unoccupied sites at “small” rate ɛ ⩾ 0. In some cases, Ω 𝑁 just includes all symmetry-equivalent sites at a single specific distance 𝑑 (in units of lattice constants) from the unoccupied site, e.g., 𝑑 = 1 (nearest-neighbor sites) where 𝑁 = 4, or 𝑑 = √5 (or √13 or…) where 𝑁 = 8. In other cases, Ω 𝑁 includes sites multiple distances from the unoccupied site, e.g., 𝑑 = {1,√2}, where 𝑁 = 8. Kinetic Monte Carlo (KMC) simulation reveals that these models exhibit a nonequilibrium discontinuous phase transition between high- and low-density states below a critical point, ɛ < ɛ c , with generic two-phase coexistence (2PC) at least for smaller 𝑁. With some exceptions, there is an approach toward mean-field behavior with increasing 𝑁 (so the regime of generic 2PC shrinks, and ɛ c approaches the mean-field value of 1/27). Additional insight into trends is provided by analysis of the exact master equations for the models via hierarchical truncation. These truncations utilize suitably tailored pair approximations which reflect the dominant nonequilibrium spatial correlations. These correlations in turn are shown to reflect the details of the autocatalytic particle creation process. For spatially heterogeneous states, the truncations produce coupled sets of lattice differential equations (LDE) which can describe orientation-dependent propagation of an interface between high- and low-density steady states for ɛ < ɛ c . Pair approximation values of 𝑝 = 𝑝 eq where the interface is stationary, and its orientation-dependence, are in semiquantitative agreement with KMC results. In conclusion, this comparison accounts for propagation failure in the LDE which complicates interpretation.
Quantum Ornstein-Zernike theory for two-temperature two-component plasmas
Laboratory plasma production almost always preferentially heats either the ions or electrons, leading to a two-temperature state. In this state, density functional theory molecular dynamic simulation is the state of the art for modeling bulk material properties. We construct a statistical mechanics model for the two temperature limit that is theoretically consistent with the molecular dynamics method. We proceed to derive the electron-ion multi-temperature quantum Ornstein-Zernike equations for the first time. This allows the construction of a two-temperature two-component plasma model using the average atom from which we can compute bulk material properties at a fraction of the computation time of the two-temperature density functional theory simulation. The accuracy of the model is benchmarked against ion pair correlation and self-diffusion results from ab initio simulation. Here, we proceed to compute the viscosity and ion thermal conductivity as a function of both ion and electron temperature.
Enhanced quantum efficiency from optical interference in alkali antimonide photocathodes: Modeling and experimental results
We present measurements of enhanced quantum efficiency (QE) in thin film alkali antimonide photocathodes from optical interference in the cathode-substrate multilayer. Modulations in the spectral response are observed over a range of visible wavelengths and are shown to increase the QE by more than a factor of two at specific wavelengths. We present a model describing the QE modulations based on the three step photoemission process incorporating cases of both constant density of states and density functional theory-derived density of states and show that the calculated results are in good agreement with the measurements. Model predictions demonstrate that QE can be enhanced by more than a factor of 5 by optimization of cathode and substrate layer thicknesses. Additionally, these calculations reveal that optical interference can yield higher quantum efficiencies in thin films compared to thick, optically dense films. We model the QE vs excitation wavelength of multiple alkali antimonide compounds at different thicknesses. We then discuss the advantages of this interference effect for electron accelerators.
One Beam, Dual Insights: Simultaneous Chemical and Structural Changes in Nanopatterned Ceria under Reaction Conditions
Ceria’s interaction with hydrogen can proceed through multiple chemical forms (hydride, hydroxyl, and oxyhydroxide-like), with consequences for the oxidation state, density, and morphology that are rarely tracked in the same evolving state. Here, in this work, we show that under mild H 2 (and H 2 and CO 2 ) environments nanopatterned ceria undergoes oxidation-state changes accompanied by hydrogen incorporation that increases the effective electron density, establishing the following order: CeO 2 Hy > CeO 2 > CeO 2–x Hy > CeO 2–x . In parallel, the surface roughens in a chemically specific manner, with the largest changes coinciding with conditions where incorporated hydrogen is driven to react with oxygen supplied either by air exposure between experiments or by added CO 2 . We obtained these insights by using a single X-ray beam to simultaneously perform ambient-pressure X-ray photoelectron spectroscopy and grazing-incidence X-ray scattering on the same sample spot. Single-mode measurements can miss key ceria–H 2 transformations relevant to optimizing ceria-based hydrogenation catalysts and supports.