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Results for “Hybrid algorithms”

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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Kekulé valence bond order in the honeycomb lattice optical Su-Schrieffer-Heeger model and its relevance to graphene

We perform sign-problem-free determinant quantum Monte Carlo simulations of the optical Su- Schrieffer-Heeger model on a half-filled honeycomb lattice. In particular, we investigate the model’s semi-metal (SM) to Kekulé Valence Bond Solid (KVBS) phase transition at zero and finite temper- atures as a function of phonon energy and interaction strength. Using hybrid Monte Carlo sampling methods we can simulate the model near the adiabatic regime, allowing us to access regions of parameter space relevant to graphene. Our simulations suggest that the SM-KVBS transition is weakly first-order at all temperatures, with graphene situated close to the phase boundary in the SM region of the phase diagram. Furthermore, our results highlight the important role bond-stretching phonon modes play in the formation of KVBS order in strained graphene-derived systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Bayesian analysis of a future β decay experiment's sensitivity to neutrino mass scale and ordering

Bayesian modeling techniques enable sensitivity analyses that incorporate detailed expectations regarding future experiments. A model-based approach also allows one to evaluate inferences and predicted outcomes, by calibrating (or measuring) the consequences incurred when certain results are reported. In this work, we present procedures for calibrating predictions of an experiment's sensitivity to both continuous and discrete parameters. Using these procedures and a new Bayesian model of the β-decay spectrum, we assess a high-precision β-decay experiment's sensitivity to the neutrino mass scale and ordering for one assumed design scenario. We find that such an experiment could measure the electron-weighted neutrino mass within ~40 meV after 1 year (90% credibility). Neutrino masses > 500 meV could be measured within ≈5 meV. Using only β decay and external reactor neutrino data, we find that next-generation β-decay experiments could potentially constrain the mass ordering using a two-neutrino spectral model analysis. By calibrating mass ordering results, we identify reporting criteria that can be tuned to suppress false ordering claims. In some cases, a two-neutrino analysis can reveal that the mass ordering is inverted, an unobtainable result for the traditional one-neutrino analysis approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Reverse order-disorder transition of Janus particles confined in two dimensions

In this study Janus particles with different patch sizes, confined to two dimensions, generate a series of patterns of interest to the field of nanoscience. Here we observe reverse melting, where for some densities the system melts under cooling. For a broad range of hydrophobic patch sizes ( 60° < θ 0 < 90°), a reentrant transition from solid to liquid and then to an ordered phase emerges as temperature (T) decreases due to the formation of rhombus chains at low T. This reentrant phase has pseudo long-range orientational order but short-range translational order, similar to a hexatic phase. Our work provides guidelines to study the melting and assembly of Janus particles in two dimensions, as well as mechanisms to generate phases with specific symmetry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Fast and scalable quantum Monte Carlo simulations of electron-phonon models

We introduce methodologies for highly scalable quantum Monte Carlo simulations of electron-phonon models, and report benchmark results for the Holstein model on the square lattice. The determinant quantum Monte Carlo (DQMC) method is a widely used tool for simulating simple electron-phonon models at finite temperatures, but incurs a computational cost that scales cubically with system size. Alternatively, near-linear scaling with system size can be achieved with the hybrid Monte Carlo (HMC) method and an integral representation of the Fermion determinant. Here, we introduce a collection of methodologies that make such simulations even faster. To combat "stiffness" arising from the bosonic action, we review how Fourier acceleration can be combined with time-step splitting. To overcome phonon sampling barriers associated with strongly-bound bipolaron formation, we design global Monte Carlo updates that approximately respect particle-hole symmetry. To accelerate the iterative linear solver, we introduce a preconditioner that becomes exact in the adiabatic limit of infinite atomic mass. Finally, we demonstrate how stochastic measurements can be accelerated using fast Fourier transforms. Here, these methods are all complementary and, combined, may produce multiple orders of magnitude speedup, depending on model details.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Triple junction solute segregation in Al-based polycrystals

Solute segregation is a crucial means of stabilizing nanostructured alloys, and at very small grain sizes, this requires consideration of triple junctions (TJs), which attain a meaningful volume fraction and thus become relevant for bulk material behavior. Here, in this study, the solute segregation spectra for grain boundary (GB) and TJ sites are calculated for a large number of dilute Al-based binary systems with available interatomic potentials. A defect-identifying algorithm is applied to quantify the average GB thickness and classify the intergranular site spectra into GB and junction subspectra. The algorithm is also applied to a hybrid electronic-level database for GB segregation of various solutes in Al, yielding polycrystalline TJ solute segregation spectra from first principles. The results suggest that TJ segregation is alloy or interatomic potential dependent and can exhibit either boundary or junction preference. With these spectra as inputs, the spectral GB segregation model gives quantitative predictions of segregation as a function of grain size, temperature, and total solute concentration, suitable for alloy screening and design.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Monte Carlo Simulations of Crystal Defects in Open Ensembles

Zero- and two-dimensional crystal defects form in open statistical ensembles, such as the grand canonical, that are usually inaccessible with conventional simulation techniques. This longstanding challenge is overcome with a new Hamiltonian Monte Carlo method that samples energy-biased gradual transformations. In conclusion, the method enables free energy calculations for nonideal point defects and the direct prediction of finite-temperature interface structures.

Grain boundaries↗
DOE OSTI2022

Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes, and it underpins the only practical numerical approach to the study of complex high-dimensional supergravity theories. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the realization of a quantum theory of gravity. Moreover, a complete numerical understanding of the holographic duality and the emergence of geometric space-time features from microscopic degrees of freedom could pave the way for new discoveries in quantum information science. Euclidean lattice Monte Carlo simulations are the de facto numerical tool for understanding the spectrum of large matrix models and have been used to test the holographic duality. However, they are not tailored to extract dynamical properties or even the quantum wave function of the ground state of matrix models. Quantum computing and deep learning provide potentially useful approaches to study the dynamics of matrix quantum mechanics. If successful in the context of matrix models, these rapidly improving numerical techniques could become the new Swiss army knife of quantum gravity practitioners. In this paper, we perform the first systematic survey for quantum computing and deep-learning approaches to matrix quantum mechanics, comparing them to lattice Monte Carlo simulations. These provide baseline benchmarks before addressing more complicated problems. In particular, we test the performance of each method by calculating the low-energy spectrum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Solving Eco-Driving Problems Using Indirect Collocation Method and Smooth Representation

Here, we discuss the eco-driving problem, considering both electric and conventional powertrains, and presents a pathway to solving it numerically using an indirect collocation method. Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve. This letter introduces smooth approximations to convert the original problem to an unconstrained (and penalized) smooth boundary-value problem. This approach eliminates the discussion of the switching structure and leads to a lightweight Newton-method-based solution procedure.

42 ENGINEERING↗
DOE OSTI2021

Rational design and testing of abiotic stress‐inducible synthetic promoters from poplar cis ‐regulatory elements

Summary Abiotic stress resistance traits may be especially crucial for sustainable production of bioenergy tree crops. Here, we show the performance of a set of rationally designed osmotic‐related and salt stress‐inducible synthetic promoters for use in hybrid poplar. De novo motif‐detecting algorithms yielded 30 water‐deficit (SD) and 34 salt stress (SS) candidate DNA motifs from relevant poplar transcriptomes. We selected three conserved water‐deficit stress motifs (SD18, SD13 and SD9) found in 16 co‐expressed gene promoters, and we discovered a well‐conserved motif for salt response (SS16). We characterized several native poplar stress‐inducible promoters to enable comparisons with our synthetic promoters. Fifteen synthetic promoters were designed using various SD and SS subdomains, in which heptameric repeats of five‐to‐eight subdomain bases were fused to a common core promoter downstream, which, in turn, drove a green fluorescent protein (GFP) gene for reporter assays. These 15 synthetic promoters were screened by transient expression assays in poplar leaf mesophyll protoplasts and agroinfiltrated Nicotiana benthamiana leaves under osmotic stress conditions. Twelve synthetic promoters were induced in transient expression assays with a GFP readout. Of these, five promoters (SD18‐1, SD9‐2, SS16‐1, SS16‐2 and SS16‐3) endowed higher inducibility under osmotic stress conditions than native promoters. These five synthetic promoters were stably transformed into Arabidopsis thaliana to study inducibility in whole plants. Herein, SD18‐1 and SD9‐2 were induced by water‐deficit stress, whereas SS16‐1, SS16‐2 and SS16‐3 were induced by salt stress. The synthetic biology design pipeline resulted in five synthetic promoters that outperformed endogenous promoters in transgenic plants.

09 BIOMASS FUELS↗
DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗
DOE OSTI2023

Improved melt model for power flow

Accelerators that drive z-pinch experiments transport current densities in excess of 1 MA/cm 2 in order to melt or ionize the target and implode it on axis. These high current densities stress the transmission lines upstream from the target, where rapid electrode heating causes plasma formation, melt, and possibly vaporization. These plasmas negatively impact accelerator efficiency by diverting some portion of the current away from the target, referred to as “current loss”. Simulations that are able to reproduce this behavior may be applied to improving the efficiency of existing accelerators and to designing systems operating at ever higher current densities. The relativistic particle-in-cell code CHICAGO ® is the primary code for modeling power flow on Sandia National Laboratories’ Z accelerator. We report here on new algorithms that incorporate vaporization and melt into the standard power-flow simulation framework. Taking a hybrid approach, the CHICAGO® kinetic/multi-fluid treatment has been expanded to include vaporization while the quasi-neutral equation-of-motion has been updated for melt at high current-densities. For vaporization, a new one-dimensional substrate model provides a more accurate calculation of electrode thermal, mass, and magnetic field diffusion as well as a means of emitting absorbed contaminants and vaporized metal ions. A quasi-fluid model has been implemented expressly to mimic the motion of imploding liners for accurate inductance histories. For melt, a multi-ion Hall-MHD option has been implemented and benchmarked against Alegra MHD. This new model is described with sufficient detail to reproduce these algorithms in any hybrid kinetic code. Physics results from the new code are also presented. A CHICAGO ® Hall-MHD simulation of a radial transmission line demonstrates that Hall physics, not included in Alegra, has no significant impact on the diffusion of electrode material. When surface contaminant desorption is mocked in as a hydrogen surface plasma, both the surface and bulk-material plasmas largely compress under the influence of the j × B force. Similar results are seen in Alegra, which also shows magnetic and material diffusion scaling with peak current. Test vaporization simulations using MagLIF and a power-flow experimental geometry show Fe + ions diffuse only a few hundred µm from the electrodes, so present models of Z power flow remain valid.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2021

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

42 ENGINEERING↗