Fermions at Finite Density in 2 + 1 Dimensions with Sign-Optimized Manifolds
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Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. Furthermore, we demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the 𝐽 1 −𝐽 2 model on a rectangular lattice.
In the last decade, the atomically-focused electron beams utilized in scanning transmission electron microscopes (STEMs) have been shown to induce a broad set of local structural transformations in materials, opening pathways for directing material synthesis and modification atom-by-atom. The mechanisms underlying these transformations remain largely unknown, due to the intractability of modeling the myriad of reaction pathways that can be accessed through high-energy electron scattering. The information on materials’ structure and dynamics that can be extracted from STEM images is similarly left underexplored. Here, we report the observation of anomalous on-site dynamics of individual silicon impurity atoms in graphene during STEM imaging. Density functional theory-based structural optimizations of anisotropically-strained molecular nanographenes reveal two distinct (but nearly degenerate) stable structures for four-fold coordinated silicon impurities, where interconversion between the two structures manifests slight changes of the silicon position within the lattice site. Implications for defect-based strain engineering in graphene are discussed.
New quaternary alloys of Ce-1 xNdxFe12 Mo-y(y) with x=0, 0.2, 0.4, 0.6, 0.8, 1 and y=0, 1.5, 2 have been prepared and magnetically hardened by melt spinning. X-ray diffraction indicates that the as-spun materials exhibit the tetragonal ThMn12-type structure. Prior to nitriding, the coercivity H-ci is less than 0.6 kOe in all alloys and is independent of Nd content, while the magnetization 4 pi M-19 (measured in an applied held of 19 kOe) and Curie temperature T-c increase with added Nd content x. The effects of nitriding pressure P, time t, and temperature TOR magnetic properties have been carefully evaluated on NdFe10Mo2 in order to identify the optimal nitriding parameters. The optimized nitriding profile was subsequently adopted to nitride the remaining samples. After nitrogenation, T-c and 4 pi M-19 have been substantially enhanced primarily due to the increased Fe-Fe exchange from nitrogen induced lattice dilation. Benefitting from the positive contribution from Nd, H-ci has been greatly improved in the Nd containing samples. As a result, Ce0.2Nd0.8Fe10Mo2 nitride features H-ci=2.9 kOe and (BH)(max) = 1.6 MGOe and Ce0.2Nd0.8Fe10.5Mo1.5 nitride demonstrates H-ci=2.5 kOe, (BH)(max) = 1.5 MGOe at room temperature, and T-c=337 degrees C. which are substantial advancements compared to the pure Ce based ThMn12-type materials previously reported. (C) 2014 Published by Elsevier B.V.
Solid-state ion conductors based on closo-polyborate anions combine high ionic conductivity with a rich array of tunable properties. Cation mobility in these systems is intimately related to the strength of the interaction with the neighboring anionic network and the energy for reorganizing the coordination polyhedra. In this paper, we explore such factors in solid electrolytes with two anions of the weakest coordinating ability, [HCB 11 H 5 Cl 6 ] – and [HCB 11 H 5 Br 6 ] – , and a total of 11 polymorphs are identified for their lithium and sodium salts. Our approach combines ab initio molecular dynamics, synchrotron X-ray powder diffraction, differential scanning calorimetry, and AC impedance measurements to investigate their structures, phase-transition behavior, anion orientational mobilities, and ionic conductivities. We find that M(HCB 11 H 5 X 6 ) (M = Li, Na, X = Cl, Br) compounds exhibit order–disorder polymorphic transitions between 203 and 305 °C and display Li and Na superionic conductivity in the disordered state. Through detailed analysis, we illustrate how cation disordering in these compounds originates from a competitive interplay among the lattice symmetry, the anion reorientational mobility, the geometric and electronic asymmetry of the anion, and the polarizability of the halogen atoms. These factors are compared to other closo-polyborate-based ion conductors to suggest guidelines for optimizing the cation–anion interaction for fast ion mobility. This study expands the known solid-state poly(carba)borate-based materials capable of liquid-like ionic conductivities, unravels the mechanisms responsible for fast ion transport, and provides insights into the development of practical superionic solid electrolytes.
Rapid data collection and modern computing resources provide the opportunity to revisit the task of optimizing the model of diffraction geometry prior to integration. A comprehensive description is given of new software that builds upon established methods by performing a single global refinement procedure, utilizing a smoothly varying model of the crystal lattice where appropriate. This global refinement technique extends to multiple data sets, providing useful constraints to handle the problem of correlated parameters, particularly for small wedges of data. Examples of advanced uses of the software are given and the design is explained in detail, with particular emphasis on the flexibility and extensibility it entails.
Nonlinear kickers (NLKs) enable off-axis injection in ultralow-emittance storage rings by providing a strong kick to the injected beam while remaining nearly transparent to the stored beam. In hybrid schemes, a conventional four-kicker bump defines the injected trajectory, and the NLK reduces the first-turn action under constrained beam offset and optics conditions. Effective operation additionally requires stable and reproducible first-turn injection trajectories. We develop a compact action–angle framework that expresses NLK dynamics in terms of Courant–Snyder invariants and yields an analytical bound on achievable action reduction. This formulation provides direct design rules for NLK placement, phase advance, injected-beam offset, and kicker field profile. Within this framework, we identify the 8-wire NLK as a practical baseline and extend its design by relaxing the square-geometry constraint, enabling inward shifting of the off-axis field peak while preserving on-axis field and gradient cancellation. Application to the NSLS-II lattice shows how aperture, pulsed-power, and mechanical constraints combine to determine a coupled design solution. Multi-turn tracking confirms that candidate NLK locations maintain sufficient stay-clear (aperture-clearance) margin, while the optimized wire geometry reduces the required current and Lorentz force load. The results establish a unified approach for NLK-assisted injection design and provide a practical pathway for upgrades in diffraction-limited storage rings.
This paper investigates the relationships between the image-gathering and image-processing systems for minimum mean-squared error estimation of scene characteristics. A stochastic optimization problem is formulated in which the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled, and noisy image data. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristics is scene restoration. Optimal edge detection is investigated. It is shown that the optimal edge detector compensates for the blurring introduced by the image-gathering optics, and, notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition.
In the search for high-performance thermoelectrics, materials such as clathrates have drawn attention due to having both glasslike low phonon thermal conductivity and crystal-like high electrical conductivity. Ba 8 Ga 16 Ge 30 (BGG) has a loosely bound guest Ba atom trapped inside rigid Ga-Ge cage structures. Avoided crossings between acoustic phonons and the flat guest atom branches have been proposed to be the source of the low lattice thermal conductivity of BGG. Ga-Ge site disorder with Ga and Ge exchanging places in different unit cells has also been reported. We used time-of-flight neutron scattering to measure the complete phonon spectrum in a large single crystal of BGG and compared these results with predictions of density functional theory to elucidate the effect of the disorder on heat-carrying phonons. Experimental results agreed much better with the calculation assuming the disorder than with the calculation assuming the ordered configuration. Although the atomic masses of Ga and Ge are nearly identical, we found that disorder strongly reduces phonon group velocities, which significantly reduces thermal conductivity. Finally, our work points to a path towards optimizing thermoelectrics.
This study presents a comparison of thermodynamically calculated phase compositions of Ag-Ga-Ge alloys at fixed temperatures with phase analysis results of alloys annealed at respective temperatures. Three isothermal sections of the Ag-Ga-Ge system at 25, 100 and 300 °C have been extrapolated using optimized thermodynamic parameters from the literature. An experimental analysis of the microstructure was carried out using optical microscopy, scanning electron microscopy and energy-dispersive spectrometry. Results obtained by x-ray powder diffraction methods (identified phases and lattice parameters) were compared with the results of the predicted phase equilibria. Good overall agreement between experimental and calculated values was obtained. The Brinell hardness and electrical conductivity of selected alloys were measured and obtained results were analyzed with respect to their overall compositions and phase constituents.
In this study, Zr x Ta 2 O 2x+5 (ZTOx) was systematically studied to determine ideal solid-state synthesis and pressureless sintering conditions. For Zr 6 Ta 2 O 17 (ZTO6) and other compositions with values of x in ZTOx the optimal synthesis temperature of 1100 °C with 1 h dwell time was optimal. XRD phase analysis of the homologous series indicated that for ZTOx, x = 5, 6, 7, and 8 were within the phase stability field while x = 4 and x = 9 were outside the phase stability field. Unit cell lattice parameters changed anisotropically with Zr:Ta ratio with an overall <0.1% change in unit cell volume across the solid solution. Some Raman modes red shifted while others blue shifted, indicating competing tensile and compressive strains. Sintering of ZTOx required a progressively higher temperature as Zr-content increased despite the same starting powder grain sizes and synthesis temperature. Higher Vickers hardness for Zr-rich (12.7–12.8 GPa) than Ta-rich Zr x Ta 2 O 2x+5 (11.6–11.7 GPa) disappeared when accounting for different sintering temperatures. Indentation toughness was similar for all compositions (1.2–1.4 MPa m 1/2 ). Properties of Zr x Ta 2 O 2x+5 series indicate tunability of Zr:Ta composition ratio while maintaining the structure and room temperature mechanical properties comparable to Hf 6 Ta 2 O 17 and 8 mol% yttria stabilized zirconia ceramics.
Atomistic spin dynamics simulations provide valuable information about the energy spectrum of magnetic materials in different phases, allowing one to identify instabilities and the nature of their excitations. However, the time cost of evaluating the dynamical correlation function S(q,t) increases quadratically as the number of spins N, leading to significant computational effort, making the simulation of large spin systems very challenging. In this work, here, we propose to use a highly optimized general matrix multiply (GEMM) subroutine to calculate the dynamical spin-spin correlation function that can achieve near-optimal hardware utilization. Furthermore, we fuse the element-wise operations in the calculation of S(q,t) into the in-house GEMM kernel, which results in further performance improvements of 44\% - 71\% on several relatively large lattice sizes when compared to the implementation that uses the GEMM subroutine in OpenBLAS, which is the state-of-the-art open source library for Basic Linear Algebra Subroutine (BLAS).
Alloy oxide heterostructures are essential for a wide variety of energy applications, including anticorrosion coatings, bifunctional catalysis, energy storage, as well as emerging platforms for multilevel nonvolatile memory and neuromorphic devices. The key role played by oxide composition, density, and stoichiometry in governing electrochemical reactions, interface structure, and ionic transport in alloy oxides remains largely unknown. This is primarily due to the lack of variable charge interatomic potential models that can adequately describe the various atomic/ionic interactions in alloy oxides within a unified framework. Here, we introduce a charge transfer ionic potential (CTIP) model for Cu/Hf/O alloy system and demonstrate its ability to accurately capture the complex potential energy surface owing to dynamically varying interactions, including metallic (Cu/Hf), ionic (Cu/Hf/Ox), mixed environment (interfaces). We leverage supervised machine learning methods powered by genetic algorithms coupled with local simplex optimization to efficiently navigate through a high-dimensional parameter space and identify an optimum set of an independent set of CTIP parameters. We train our model against an extensive first-principles based data set that includes lattice constants, cohesive energies, equations of state, and elastic constants of various experimentally observed polymorphs of hafnia, copper oxide, hafnium, as well as copper. Our machine-learned CTIP model captures the structure, elastic properties, thermodynamics, and energetic ordering of various polymorphs. It also accurately predicts the surface properties of both oxides and their metals. To demonstrate the suitability of our CTIP model for investigating dynamic processes, we employed it to identify the atomistic scale mechanisms associated with the initial nanoscale oxidation and surface oxide growth on Cu-doped hafnia surfaces. Here, our machine-learned CTIP model can be used to probe the dynamic response of Cu/Hf/O alloy interfaces subjected to external stimuli (e.g., electric field, pressure, temperature, strain, etc.) and a variety of atomistic phenomena including the dynamics of switching in emerging neuromorphic platforms.
Here we report that CdTe alloying and Sb doping increase the density-of-states effective mass and introduce endotaxial nanostructuring in n-type PbTe, resulting in enhanced thermoelectric performance. A prior theoretical prediction for the presence of resonance states in the conduction band of this system, however, could not be confirmed. An amount of 3 mol % CdTe alloying widens the band gap of PbTe by 50%, leading to enhanced carrier effective mass and Seebeck coefficient. This effect is even more pronounced at high temperatures where the solubility of CdTe increases. At 800 K, when the carrier concentration is the same (4 × 10 19 cm -3 ), the Seebeck coefficient of CdTe-alloyed PbTe is -195 μV K -1 , 16% higher than that of the Cd-free control sample (-168 μV K -1 ). Sb doping considerably increases the electron concentration of Pb 0.97 Cd 0.03 Te, giving rise to optimized power factors of ~17 μW cm -1 K -2 at 800 K. More importantly, Sb induces strained endotaxial nanostructures evenly distributed in the matrix. Furthermore, these Sb-rich nanostructures account for the ~40% reduction in the lattice thermal conductivity over the whole measured temperature range. As a result, a maximum ZT of 1.2 is attained at 750 K in 0.5 mol % Sb-doped Pb 0.97 Cd 0.03 Te alloys.
Here we use density functional theory based ab initio calculations to investigate the structural, vibrational, magnetic, and electronic properties of the layered ferromagnet Cr 2 Ge 2 Te 6 (CGT) that has attracted attention for potential spintronic applications. We optimize the structure for a fixed c/a ratio. Our results are in excellent agreement with experimental data on structure, phonons, and electronic properties. The use of van der Waals interactions and relativistic spin-orbit coupling yields accurate lattice constants and interlayer distances. Simultaneously, the adopted theoretical methods lead to a rigorous description of the vibrational normal modes, as well as the valence bands, that are in excellent agreement with Raman spectroscopy and angle-resolved photoemission spectroscopy spectra, respectively. The magnetic moment is slightly overestimated, and the magnetic anisotropy has the correct sign but is greater in magnitude than that in experiment. We find that inclusion of the on-site Coulomb repulsion on Cr−3d orbitals worsens agreement with experiment, in contrast to previous studies. Our results provide a path toward ab initio analysis of magnetic heterostructures based on CGT.
The successful operation of the future Electron-Ion Collider is contingent on maintaining high hadron beam polarization up to 275 GeV. The Hadron Storage Ring lattice, however, features a symmetry-breaking interaction region that excites strong, nonsystematic spin resonances, posing a significant threat to polarization preservation. This paper systematically investigates two complementary strategies to ensure high polarization transmission. The first method involves optimizing the vertical betatron phase advance between Siberian snakes to orchestrate a cancellation of depolarizing kicks across the ring. We demonstrate through simulations that both dynamic and fixed-optics solutions based on this principle can successfully preserve polarization through the strongest resonances. The second, more powerful approach involves optimizing the snake rotation axes to suppress resonance driving terms at their source. We revisit established symmetric configurations, such as the Lee-Courant schemes, and introduce a novel, highly symmetric “Doubly Lee-Courant” (DLC) scheme, which enforces a local 𝜋 spin phase advance across every consecutive pair of snakes. Our analysis reveals a clear performance hierarchy, with the DLC configuration providing an exceptionally robust and energy-insensitive baseline for polarization preservation. We conclude that a hybrid strategy, using a DLC snake scheme as a symmetric foundation and betatron phase tuning for fine corrections, offers the most effective path forward for the EIC and future high-energy polarized-beam facilities.
Serial femtosecond crystallography (SFX) uses an X-ray free-electron laser to extract diffraction data from crystals not amenable to conventional X-ray light sources owing to their small size or radiation sensitivity. However, a limitation of SFX is the high variability of the diffraction images that are obtained. As a result, it is often difficult to determine optimal indexing and integration parameters for the individual diffraction images. Presented here is a software package, called IOTA , which uses a grid-search technique to determine optimal spot-finding parameters that can in turn affect the success of indexing and the quality of integration on an image-by-image basis. Integration results can be filtered using a priori information about the Bravais lattice and unit-cell dimensions and analyzed for unit-cell isomorphism, facilitating an improvement in subsequent data-processing steps.
We have investigated the early stages of evolution of highly strained 2-D InAs layers and 3-D InAs islands grown by metal-organic chemical vapor deposition (MOCVD) on (100) and (111) B GaAs substrates. The InAs epilayer / GaAs substrate combination has been chosen because the lattice-mismatch is severe (approx. 7.20%), yet these materials are otherwise very similar. By examining InAs-on-GaAs composites Instead of the more common In(x)Ga(1-x)As alloy, we remove an additional degree of freedom (x) and thereby simplify data interpretation. A matrix of experiments is described in which the MOCVD growth parameters -- susceptor temperature, TMIn flux, and AsH3 flux -- have been varied over a wide range. Scanning electron microscopy, atomic force microscopy, transmission electron microscopy, and electron microprobe analysis have been employed to observe the thin film surface morphology. In the case of 3-D growth, we have extracted activation energies and power-dependent exponents that characterize the nucleation process. As a consequence, optimized growth conditions have been identified for depositing approx. 250 A thick (100) and (111)B oriented InAs layers with relatively smooth surfaces. Together with preliminary data on the strain relaxation of these layers, the above results on the evolution of thin InAs films indicate that the (111)B orientation is particularly promising for yielding lattice-mismatched films that are fully relaxed with only misfit dislocations at the epilayer / substrate interface.