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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Accelerating the convergence of auxiliary-field quantum Monte Carlo in solids with optimized Gaussian basis sets

We investigate the use of optimized correlation-consistent Gaussian basis sets for the study of insulating solids with auxiliary-field quantum Monte Carlo (AFQMC). The exponents of the basis set are optimized through the minimization of the second-order Møller–Plesset perturbation theory (MP2) energy in a small unit cell of the solid. We compare against other alternative basis sets proposed in the literature, namely, calculations in the Kohn–Sham basis and in the natural orbitals of an MP2 calculation. We find that our optimized basis sets accelerate the convergence of the AFQMC correlation energy compared to a Kohn–Sham basis and offer similar convergence to MP2 natural orbitals at a fraction of the cost needed to generate them. We also suggest the use of an improved, method independent, MP2-based basis set correction that significantly reduces the required basis set sizes needed to converge the correlation energy. With these developments, we study the relative performance of these basis sets in LiH, Si, and MgO and determine that our optimized basis sets yield the most consistent results as a function of volume. Using these optimized basis sets, we systematically converge the AFQMC calculations to the complete basis set and thermodynamic limit and find excellent agreement with experiment for the systems studied. Although we focus on AFQMC, our basis set generation procedure is independent of the subsequent correlated wavefunction method used.

Morales, Miguel A. (ORCID:0000000263893067)↗
DOE OSTI2024

A multifidelity Bayesian optimization method for inertial confinement fusion design

Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, these high-fidelity models are by themselves insufficient for optimal experimental design, because their computational cost remains too high to efficiently and effectively explore the numerous parameters required to describe a typical experiment. Therefore, traditionally, ICF design has relied on low-fidelity modeling to initially identify potentially interesting design regions, which are then subsequently explored via selected high-fidelity modeling. In this paper, we demonstrate that this two-step approach can be insufficient: even for simple design problems, a two-step optimization strategy can lead high-fidelity searching toward incorrect regions and consequently waste computational resources on parameter regimes far away from the true optimal solution. We reveal that a primary cause of this behavior in ICF design problems is the presence of low-fidelity optima in different regions of the parameter space far away from high-fidelity optima. To address this issue, we propose an iterative multifidelity Bayesian optimization method based on Gaussian Process Regression that leverages both low- and high-fidelity models simultaneously. We demonstrate, using both two- and eight-dimensional ICF test problems, that our algorithm can effectively utilize both low-fidelity and high-fidelity models to refine the designs. This approach proves to be more efficient than relying solely on high-fidelity modeling for optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Incident beam optics optimization for the single crystal neutron diffractometer Pioneer with a polarized beam option

Pioneer, a next-generation single-crystal neutron diffractometer, is under development for Oak Ridge National Laboratory’s Second Target Station. Designed to address a wide range of scientific questions, Pioneer will deliver homogeneous neutron beams with customizable size and divergence and provide a polarized beam option. This article introduces its incident beam optics, highlighting the optimization methodology and the simulated performance. Pioneer will utilize a modified elliptical-straight guide for neutron transport and deploy slit packages and insertable apertures to control beam size and divergence. The optimized guide geometry matches the optimal-and-full-sample-illumination condition, and the beam control system effectively filters out unwanted neutrons while preserving the desired ones. In addition, we have found that polygon-approximated guides provide satisfactory transport efficiency and beam homogeneity, eliminating the need for truly curved guides. Further, to enhance neutronics performance and reduce cost, the coatings of supermirror elements are individually optimized to the lowest half-integer m-values that are sufficient to deliver the desired neutrons. After evaluating polarizing V-cavities and 3 He spin filters over the default polarized wavelength band of 1.2–5.5 Å, we selected a translatable multichannel polarizing V-cavity as the incident beam polarizer. Strategically placed at a location where the beam divergence is low and a large in-guide gap has negligible impact on transport efficiency, the optimized V-cavity achieves an average P 2 T of ~35%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Nearly optimal quantum control: an analytical approach

We propose here nearly optimal control strategies for changing the states of a quantum system. We argue that quantum control optimization can be studied analytically within some protocol families that depend on a small set of parameters for optimization. This optimization strategy can be preferred in practice because it is physically transparent and does not lead to combinatorial complexity in multistate problems. Finally, as a demonstration, we design optimized control protocols that achieve switching between orthogonal states of a naturally biased quantum two-level system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Optimizing multi-modal, non-axisymmetric plasma response metrics with additional coil rows on DIII-D

The expansion of the DIII-D tokamak operational space via the addition of new non-axisymmetric coil rows is investigated using the linear MHD plasma response code MARS-F. The plasma response to magnetic perturbations imposed by five distinct low- and high-field side coil geometries is modeled for toroidal mode numbers up to n = 6. A set of linear (~δB^2: resonant, kink mode amplitude) and quadratic (~δB: core and edge integrated NTV torque) plasma metrics are defined and compared directly for the various coil geometries. Coil size is also scanned as a secondary optimization variable. An in-vessel coil located at the low-field side midplane is identified as the most efficient plasma response actuator across all metrics and toroidal mode numbers. The multi-row combination of this new midplane (M) coil with the existing DIII-D non-axisymmetric coil set is considered in numerical optimization studies to quantify the impact of this new coil on modeled plasma response metrics. The addition of the M coil is shown to significantly increase the actuator efficiency in maximizing individual plasma response metrics as well as maximizing multi-modal ratios of response metrics, e.g. maximizing resonant modes or edge NTV torque while minimizing kink modes or core NTV torque. The ex-vessel midplane (C) coil is also observed to have an outsize effect on multi-modal optimization due to its uniquely narrow poloidal spectra, and the expansion of optimization space due to the combined use of the two existing in-vessel coil rows with both the M and C coils is notably larger than either coil's individual contribution even though both share similar poloidal locations. This four-row system is expected to greatly extend abilities to understand and optimize the control of transients and rotation on the DIII-D tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Integrating novel stellarator single-stage optimization algorithms to design the Columbia stellarator experiment

Abstract The Columbia Stellarator eXperiment (CSX), currently being designed at Columbia University, aims to test theoretical predictions related to QA plasma behavior, and to pioneer the construction of an optimized stellarator using three-dimensional, non-insulated high-temperature superconducting (NI-HTS) coils. The magnetic configuration is generated by a combination of two circular planar poloidal field (PF) coils and two 3D-shaped interlinked (IL) coils, with the possibility to add windowpane coils to enhance shaping and experimental flexibility. The PF coils and vacuum vessel are repurposed from the former Columbia Non-Neutral Torus experiment, while the IL coils will be custom-wound in-house using NI-HTS tapes. To obtain a plasma shape that meets the physics objectives with a limited number of coils, novel single-stage optimization techniques are employed, optimizing both the plasma and coils concurrently, in particular targeting a tight aspect ratio QA plasma and minimized strain on the HTS tape. Despite the increased complexity due to the expanded degrees of freedom, these methods successfully identify optimized plasma geometries that can be realized by coils meeting engineering specifications. This paper discusses the derivation of the constraints and objectives specific to CSX, and describe how two recently developed single-stage optimization methodologies are applied to the design of CSX. A set of selected configurations for CSX is then described in detail.

Baillod, A. (ORCID:0000000303529180)↗
DOE OSTI2017

Stochastic optimization of GeantV code by use of genetic algorithms

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Online accelerator optimization with a machine learning-based stochastic algorithm

Abstract Online optimization is critical for realizing the design performance of accelerators. Highly efficient stochastic optimization algorithms are needed for many online accelerator optimization problems in order to find the global optimum in the non-linear, coupled parameter space. In this study, we propose to use the multi-generation Gaussian process optimizer for online accelerator optimization and demonstrate that the algorithm is significantly more efficient than other stochastic algorithms that are commonly used in the accelerator community.

Zhang, Zhe (ORCID:0000000281430381)↗
DOE OSTI2023

From Optimization to Sampling Through Gradient Flows

Optimization and sampling algorithms play a central role in science and engineering as they enable finding optimal predictions, policies, and recommendations, as well as expected and equilibrium states of complex systems. The notion of “optimality” is formalized by the choice of an objective function, while the notion of an “expected” state is specified by a probabilistic model for the distribution of states. Optimizing rugged objective functions and sampling multimodal distributions is computationally challenging, especially in high-dimensional problems. Here, for this reason, many optimization and sampling methods have been developed by researchers working in disparate fields such as Bayesian statistics, molecular dynamics, genetics, quantum chemistry, machine learning, weather forecasting, econometrics, and medical imaging.

Trillos, N. García↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2026

Characterization and automated optimization of laser-driven proton beams from converging liquid sheet jet targets

Compact, stable, and versatile laser-driven ion sources hold great promise for applications ranging from medicine to materials science and fundamental physics. While single-shot sources have demonstrated favorable beam properties, including the peak fluxes necessary for several applications, high-repetition-rate operation will be necessary to generate and sustain the high average flux needed for many of the most exciting applications of laser-driven ion sources. Further, to navigate through the high-dimensional space of laser and target parameters toward experimental optima, it is essential to develop ion acceleration platforms compatible with machine learning techniques and capable of autonomous real-time optimization. Here, we present a multi-Hz ion acceleration platform employing a liquid sheet jet target. We characterize the laser-plasma interaction and the laser-driven proton beam across a variety of key parameters governing the interaction using an extensive suite of online diagnostics. We also demonstrate real-time, closed-loop optimization of the ion beam maximum energy by tuning the laser wave front using a Bayesian optimization scheme. This approach increased the maximum proton energy by 11% compared to a manually optimized wave front by enhancing the energy concentration within the laser focal spot, demonstrating the potential for closed-loop optimization schemes to tune future ion accelerators for robust high-repetition-rate operation.

Glenn, G. D. [SLAC National Accelerator Laboratory↗
DOE OSTI2020

Optimal control of coupled quantum systems based on the first-order Magnus expansion: Application to multiple dipole-dipole-coupled molecular rotors

This paper presents a method for performing approximate optimal control simulations for quantum systems with multiple coupled degrees of freedom. In this work, the time evolution is simulated using the first-order Magnus expansion in the interaction picture, where the couplings between different degrees of freedom are treated as the perturbation. A numerical implementation procedure is presented that leverages upon pairwise couplings and the separability of the zeroth-order time evolution operator to achieve a reduced computational cost, which is analyzed with respect to the number of degrees of freedom. The formulation is compatible with gradient-free methods to optimize the control field, and a stochastic hill climbing algorithm is adopted for this purpose. As illustrations, optimal control simulations are performed for systems of two and three dipole-dipole-coupled molecular rotors under the influence of a control field. For the two-rotor system, the field is optimized to achieve either orientation or entanglement objectives. For the three-rotor system, the field is optimized either to orient all three rotors in the same direction or to orient one rotor in a particular direction while the other two rotors point in the opposite direction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Efficient frequency allocation for superconducting quantum processors using improved optimization techniques

Building on previous research on frequency allocation optimization for superconducting circuit quantum processors, this work incorporates several techniques to improve overall solution quality. Here, we introduce constraints and imposed edgewise differences help to improve the optimization results. We also introduce optimization variables for the orientation of each edge, defined as the direction from the control qubit to the target qubit, to be chosen during optimization. To scale up to larger processors, multimodule designs are employed with various boundary conditions, thereby enhancing the collective yield. These enhancements allow for greater flexibility in processor design by eliminating the need for handpicked orientations. We support the efficient assembly of large processors with dense connectivity by choosing the best boundary conditions. Examples demonstrate that, at low computational cost, this optimization approach finds a frequency configuration for a square chip with over 1000 qubits and over 10% yield at much larger dispersion levels than required by previous approaches.

Zhang, Zewen [Argonne National Laboratory (ANL), A↗
DOE OSTI2017

Optimized pulses for Raman excitation through the continuum: Verification using the multiconfigurational time-dependent Hartree-Fock method

Here, we have verified a mechanism for Raman excitation of atoms through continuum levels previously obtained by quantum optimal control using the multiconfigurational time-dependent Hartree-Fock (MCTDHF) method. For the optimal control, which requires running multiple propagations to determine the optimal pulse sequence, we used the computationally inexpensive time-dependent configuration interaction singles (TDCIS) method. TDCIS captures all of the necessary correlation of the desired processes but assumes that ionization pathways reached via double excitations are not present. MCTDHF includes these pathways and all multiparticle correlations in a set of time-dependent orbitals. The mechanism that was determined to be optimal in the Raman excitation of the Ne 1s 2 2s 2 2p 5 3p 1 valence state via the metastable 1s 2 2s 1 2p 6 3p 1 resonance state involves a sequential resonance-valence excitation. First, a long pump pulse excites the core-hole state, and then a shorter Stokes pulse transfers the population to the valence state. This process represents the first step in a multidimensional x-ray spectroscopy scheme that will provide a local probe of valence electronic correlations. Although at the optimal pulse intensities at the TDCIS level of theory the MCTDHF method predicts multiple ionization or excitation ionization of the atom, at slightly lower intensities (reduced by a factor of about 4) the TDCIS mechanism is shown to hold qualitatively. Quantitatively, the MCTDHF populations are reduced from the TDCIS calculations by a factor of 4.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Three-dimensional alignment of asymmetric-top molecules induced by polarization-shaped optimal laser pulses

Optimal control simulation is applied to numerically design nonresonant laser pulses that maximize the degrees of three-dimensional (3D) alignment of SO 2 using the lowest-order induced-dipole interaction. In our trials, combinations of more than two mutually orthogonal, linearly polarized subpulses are always obtained as the optimal solutions. Each subpulse in the optimal pulses impulsively excites the rotational wave packet. The optimal pulses effectively cooperate with the rotational dynamics up to only a few partial revival timings owing to the rotational dephasing that determines the effective control periods. The control mechanisms are interpreted in terms of the time derivatives of the expectation values of the squares of the direction cosines, that of the rotational energy, and the interplay between them. We find a special and important role of the last subpulses as they align the molecular axes using the interaction through the two smallest polarizability components, while the other subpulses excite the rotational wave packet mainly through the largest polarizability component. Lastly, the control pulses composed of the specified number of subpulses are also numerically optimized by actively utilizing the instantaneous penalty to systematically show the superiority of the use of more than two subpulses over that of two subpulses, the latter of which leads to the saturation of the degree of 3D alignment as a function of total fluence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Equidistant optimization of elliptical superconducting rf standing wave cavities

A record accelerating rate was achieved earlier in standing wave (SW) SRF cavities when their shape was optimized for a lower peak surface magnetic field sacrificing the peak surface electric field. In view of new materials with higher limiting magnetic fields, expected for SRF cavities, in the first line of the Nb 3 Sn, the approach to optimization of cavity shape should be revised. A method of equidistant optimization, offered earlier for traveling wave (TW) cavities is applied to SW cavities. It is shown here that without limitation by magnetic field, the maximal accelerating rate is defined not only by limitations of the electric field but to a significant degree by the cavity shape. For example, for a cavity with the aperture radius of $R$ $a$ = 35 mm, the minimal ratio of the peak surface electric field to the accelerating rate is about $E$ pk / $E$ acc = 1.54. So, with the maximal surface field experimentally achieved $E$ pk ~ 125 MV / m, the maximal achievable accelerating rate is about 80 MeV / m even if there are no restrictions by the magnetic field. Optimized cavity shapes with and without limitations by a magnetic field are presented. Another opportunity—optimization for a low magnetic field, is opening for the same material, Nb 3 Sn, with the purpose of having a high-quality factor and increased accelerating rate that can be used for industrial linacs with cryocooler-based cooling scheme.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Unified, Geometric Framework for Nonequilibrium Protocol Optimization

Controlling thermodynamic cycles to minimize the dissipated heat is a long-standing goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation. Furthermore, we show that the thermodynamic metric—determined via a linear response approach—can be directly derived from the same objective function that is optimized in the optimal transport problem, thus providing a unified perspective on thermodynamic geometries. As a result, we investigate this unified geometric framework in two model systems and observe that our procedure for optimizing control protocols is robust beyond linear response.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimal Twirling Depth for Classical Shadows in the Presence of Noise

The classical shadows protocol is an efficient strategy for estimating properties of an unknown state p using a small number of state copies and measurements. In its original form, it involves twirling the state with unitaries from some ensemble and measuring the twirled state in a fixed basis. It was recently shown that for computing local properties, optimal sample complexity (copies of the state required) is remarkably achieved for unitaries drawn from shallow depth circuits composed of local entangling gates, as opposed to purely local (zero depth) or global twirling (infinite depth) ensembles. Here, we consider the sample complexity as a function of the depth of the circuit, in the presence of noise. We find that this noise has important implications for determining the optimal twirling ensemble. Under fairly general conditions, we (i) show that any single-site noise can be accounted for using a depolarizing noise channel with an appropriate damping parameter f, (ii) compute thresholds f th at which optimal twirling reduces to local twirling for Pauli operators, (iii) nth order Renyi entropies (n ≥2), and (iv) provide a meaningful upper bound t max on the optimal circuit depth for any finite noise strength f, which applies to observables and entanglement entropy measurements. In conclusion, these thresholds strongly constrain the search for optimal strategies to implement shadow tomography and are easily tailored to the experimental system at hand.

97 MATHEMATICS AND COMPUTING↗