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At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14,341 locations including 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering is applied to the geochemical data matrix that automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivity as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Chemistry, Structure, and Function of Lone Pairs in Extended Solids

The lone pair has been a known feature of the electronic structure of molecules for over 100 years. Beginning with the pioneering work of Lewis and others that was later developed into useful guidelines for predicting molecular structure, lone pairs and their steric consequences are now taught at the very earliest stages of a chemistry education. In the crystalline solid state, lone pairs have perhaps had a less visible yet equally consequential role, with a significant impact on a range of properties and functionalities. Important properties associated with s 2 electron-derived lone pairs include their role in creating conditions favorable for ion transport, in the formation and correlation of local dipoles and the resulting polar behavior leading to ferroics and multiferroics, in increasing the refractive index of glass, in reducing the thermal conductivity of thermoelectric materials, and in breaking local symmetry permitting second-harmonic light generation.. In recent years, the role of the lone pair in developing the electronic structure of some topological quantum materials has also been recognized. While structural distortions due to lone pairs have traditionally been characterized through their crystallography, recent advances in scattering and spectroscopy have revealed the presence of local lone pair-driven distortions that do not correlate over long length scales. The role of these crystallographically “hidden” lone pairs, their detection, and their impact on properties have become a growing body of work in the literature. Hidden lone pairs are an effective argument for considering a role for lone pairs that goes beyond their being objects that occupy space in the coordination polyhedra of cations. This Account introduces the chemistry of lone pairs in extended crystalline solids, including a discussion of when they are stereochemically active, how they manifest in the structure, and how their chemistry can be tuned by the chemical environment around them. Eventually, all of these factors work in unison to help develop and tune properties of interest. Certain specific examples of structure–property relationships in materials that are driven by lone pair behavior are described here, including the potential impact of lone pairs on the optical and electronic properties of hybrid halide perovskite compounds that are relevant to their photovoltaic applications. Here, we highlight the role of lone pairs in the dielectric behavior of geometrically frustrated pyrochlores, the temperature-dependent optoelectronic behavior of halide perovskites, the polar phase transitions in lead-free ferroelectric perovskites, and the compositional insulator-to-metal transition in ruthenium pyrochlores. The theme underpinning this Account is that the lone pair can be considered to be a powerful design element for a broad range of material function.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Near-Field Corrosion Interactions between Glass and Corrosion Resistant Alloys

This study explores the corrosion interactions between model nuclear waste glass materials and corrosion resistant alloys, under conditions that simulate the near field of a nuclear waste repository. The interactions between the corrosion of stainless steel (SS) 316, alloy G30, or alloy 625, and international simple glass or soda-lime silica glass are systematically studied. The dissimilar materials were exposed in close proximity to each other in different electrolytes at 90 oC. After exposure, the glass surface exposed near metals showed different regimes of corrosion, with distinct surface morphologies and chemistries that were likely affected by the local environment created by the localized corrosion of metals. Surface and solution analyses showed that the corrosion rate of glass was enhanced by the presence of metals. Infrared spectroscopy data suggested the local build-up of stresses in the contact area of glass, which may lead to the mechanical instability of the glass alteration layer. On the other hand, the effect of glass on metal corrosion is strongly dependent on the leaching solution. In electrolytes containing abundant aggressive anions such as Cl-, glass seems to suppress the localized corrosion of SS by the precipitation of a Si-rich surface film that protects the SS substrate from solutions. However, in less aggressive electrolytes, the corrosion rate of SS was increased by the presence of glass corrosion products. Overall, our study showed that the hidden and localized damage on glass in contact with metals may enhance the release of incorporated radionuclides compared to typical uniform glass corrosion. Therefore, more attention should be paid to the corrosion interactions between metals and glass under repository conditions.

Guo, Xiaolei↗

DOE OSTI2020

A quantum liquid of magnetic octupoles on the pyrochlore lattice

Spin liquids are highly correlated yet disordered states formed by the entanglement of magnetic dipoles. Theories define such states using gauge fields and deconfined quasiparticle excitations that emerge from a local constraint governing the ground state of a frustrated magnet. For example, the ‘2-in–2-out’ ice rule for dipole moments on a tetrahedron can lead to a quantum spin ice in rare-earth pyrochlores. However, $f$-electron ions often carry multipole degrees of freedom of higher rank than dipoles, leading to intriguing behaviours and ‘hidden’ orders. In this paper we show that the correlated ground state of a Ce 3+ -based pyrochlore, Ce 2 Sn 2 O 7 , is a quantum liquid of magnetic octupoles. Our neutron scattering results are consistent with a fluid-like state where degrees of freedom have a more complex magnetization density than that of magnetic dipoles. The nature and strength of the octupole–octupole couplings, together with the existence of a continuum of excitations attributed to spinons, provides further evidence for a quantum ice of octupoles governed by a ‘2-plus–2-minus’ rule. Our work identifies Ce 2 Sn 2 O 7 as a unique example of frustrated multipoles forming a ‘hidden’ topological order, thus generalizing observations on quantum spin liquids to multipolar phases that can support novel types of emergent fields and excitations.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Berry curvature memory through electrically driven stacking transitions

In two-dimensional layered quantum materials, the interlayer stacking order determines both crystalline symmetry and quantum electronic properties such as Berry curvature, topology and electron correlation. Electrical stimuli can strongly influence quasi-particle interactions and the free energy landscape, thus making it possible to access hidden stacking orders with novel quantum properties and enabling dynamic engineering of these attributes. In this paper, we demonstrate electrically driven stacking transitions and a new type of nonvolatile memory based on Berry curvature in few-layer WTe 2 . The interplay of out-of-plane electric fields and electrostatic doping controls in-plane interlayer sliding and creates multiple polar and centrosymmetric stacking orders. In-situ nonlinear Hall transport reveals such stacking rearrangements result in a layer-parity-selective Berry curvature memory in momentum space, where the sign reversal of the Berry curvature only occurs in odd layer crystals. Our findings open an avenue towards exploring coupling between topology, electron correlations, and ferroelectricity in hidden stacking orders and demonstrate a new low energy cost, electrically-controlled topological memory in the atomically thin limit.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Physics-informed machine learning

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Furthermore, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗

DOE OSTI2020

Generalized Grad–Shafranov equation for non-axisymmetric MHD equilibria

The structure of static MHD equilibria that admit continuous families of Euclidean symmetries is well understood. Such field configurations are governed by the classical Grad–Shafranov equation, which is a single elliptic partial differential equation in two space dimensions. By revealing a hidden symmetry, here we show that in fact all smooth solutions of the equilibrium equations with non-vanishing pressure gradients away from the magnetic axis satisfy a generalization of the Grad–Shafranov equation. In contrast to solutions of the classical Grad–Shafranov equation, solutions of the generalized equation are not automatically equilibria, but instead only satisfy force balance averaged over the one-parameter hidden symmetry. We then explain how the generalized Grad–Shafranov equation can be used to reformulate the problem of finding exact three-dimensional smooth solutions of the equilibrium equations as finding an optimal volume-preserving symmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Periodic Korteweg–de Vries soliton potentials generate quasisymmetric magnetic field strength in a finite plasma- β equilibrium

Quasisymmetry (QS) is a hidden symmetry of the magnetic field strength, B , that enables effective confinement of charged particles in a fully three-dimensional (3D) toroidal plasma equilibrium. Such equilibria are typically modeled by the ideal magnetohydrostatic (MHS) equations. The nonlinear, overdetermined nature of the QS MHS equations severely complicates our understanding of the interplay between 3D shaping, equilibrium properties such as pressure and rotational transform, and B . Progress has been made through expansions near the magnetic axis; however, a more comprehensive theory is desirable. Using a combination of analysis and regression on a large dataset of numerically optimized quasisymmetric stellarators, we demonstrate that there is a hidden lower dimensionality of B on a magnetic flux surface with connections to the theory of periodic solitons. We show that B on a flux surface is determined by three or at most four flux functions, each of which determines a critical value of the derivative of B along the field line. While being consistent with the near-axis models, our results are global and hold even on the last closed flux surface.

Differential geometry↗

DOE OSTI2023

Unified theory of hastatic order and antiferromagnetism in URu 2 Si 2

The hidden-order phase of URu 2 Si 2 has eluded identification for over 35 years. A compelling proposal that explains the Ising heavy-fermion nature of the material is hastatic order: a symmetry-breaking heavy Fermi liquid arising from a spinorial microscopic hybridization. The original hastatic proposal cannot microscopically model the pressure-induced antiferromagnetic phase; and while it predicts a spinorial order parameter, it does not provide any detectable signatures of the spinorial nature. Here, we present a more realistic microscopic model of hastatic order in URu 2 Si 2 based on two conducting electron bands and explore its phase diagram in detail. Our model nontrivially preserves the Ising heavy-fermion physics of the original, while allowing us to tune between the antiferromagnet and hidden order using pressure analogs and magnetic field. Furthermore, our model is also consistent with recent phenomenological predictions of not one, but two, vector order parameters associated with the spinorial order that provide the first microscopic predictions for detecting the spinorial nature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Fine structure of pentaquark multiplets in the dynamical diquark model

In this work, we apply the dynamical diquark model to predict the spectrum of hidden-charm pentaquark states in both unflavored and open-strange sectors. Using only Hamiltonian parameters introduced in the tetraquark S - and P -wave multiplets, the model naturally produces the level spacing supported by the most recent LHCb results for P c structures. Furthermore, using model inputs obtained from data of hidden-charm, open-strange tetraquarks ( Z c s ), we predict the spectrum of P c s states, including the recently observed P c s ( 4459 ) . We find all pentaquark candidates observed to date to belong to the 1 P multiplet and hence have positive parity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Supersymmetry with scalar sequestering

Supersymmetric models with a strongly interacting superconformal hidden sector (HS) may drive soft supersymmetry (SUSY) breaking scalar masses, bilinear soft term B μ and Higgs combinations m H u , d 2 + μ 2 to small values at some intermediate scale, leading to unique sparticle mass spectra along with possibly diminished fine-tuning in spite of a large superpotential μ parameter. We set up a computer code to calculate such spectra, which are then susceptible to a variety of constraints: (1) possible charge-or-color breaking (CCB) minima in the scalar potential, (2) unbounded from below (UFB) scalar potential, (3) improper electroweak symmetry breaking, (4) a charged or sneutrino lightest SUSY particle (LSP), (5) generating m h ∼ 125 GeV , (6) consistency with LHC sparticle mass limits, and (7) naturalness. We find this bevy of constraints leaves little or no viable parameter space for the case where hidden sector dynamics dominates minimal supersymmetric standard model (MSSM) running, even for the case of nonuniversal gaugino masses. For the case with moderate HS running with comparable MSSM running, and with universal gaugino masses, then the fine-tuning is ameliorated, but nonetheless remains high. Viable spectra with moderate HS running and with low fine-tuning and large μ can be found for nonuniversal gaugino masses. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2021

Role of Equilibrium Fluctuations in Light-Induced Order

Engineering novel states of matter with light is at the forefront of materials research. An intensely studied direction is to realize broken-symmetry phases that are “hidden” under equilibrium conditions but can be unleashed by an ultrashort laser pulse. Despite a plethora of experimental discoveries, the nature of these orders and how they transiently appear remain unclear. To this end, we investigate a nonequilibrium charge density wave (CDW) in rare-earth tritellurides, which is suppressed in equilibrium but emerges after photoexcitation. Using a pump-pump-probe protocol implemented in ultrafast electron diffraction, we demonstrate that the light-induced CDW consists solely of order parameter fluctuations, which bear striking similarities to critical fluctuations in equilibrium despite differences in the length scale. By calculating the dynamics of CDW fluctuations in a nonperturbative model, we further show that the strength of the light-induced order is governed by the amplitude of equilibrium fluctuations. Overall, these findings highlight photoinduced fluctuations as an important ingredient for the emergence of transient orders out of equilibrium. Our results further suggest that materials with strong fluctuations in equilibrium are promising platforms to host hidden orders after laser excitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Echo state network for coarsening dynamics of charge density waves

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Furthermore, our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.

2-dimensional systems↗

DOE OSTI2025

Higgs potential from instantons

We propose that the Higgs potential, a key element in our understanding of nature, is partially generated by the instantons of new confining dynamics, perhaps from a hidden sector. In this picture, while the Higgs itself is a fundamental field, it controls the strength of the nonperturbative interactions that give rise to its potential. This setup can be realized in “Twin Higgs” hierarchy models, where the twin strong dynamics confines at or above the weak scale, but large quantum corrections to the Higgs potential are avoided. We examine a simple setup in which this instanton contribution is augmented by a quartic term, which is sufficient for a realistic electroweak symmetry breaking mechanism. The minimum of the potential is given by the Lambert 𝑊 0 function, with these assumptions. We discuss the predictions of this model and how it may be tested through measurements of the Higgs self coupling. Given the connection with nontrivial dynamics, one may also consider the prospects of accessing hidden sector states at colliders; this seems to be typically challenging in our setup. Symmetry restoration in the early Universe in this scenario is briefly examined. We also comment on the possible connection of our general setup with recent work on the physics of field space end points.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Analyzing the Quantum Approximate Optimization Algorithm: Ansätze, Symmetries, and Lie Algebras

The quantum approximate optimization algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the underlying algebraic properties of three QAOA ansätze for the maximum-cut problem on connected graphs, while focusing on the generated Lie algebras as well as their invariant subspaces. Specifically, we analyze the standard QAOA ansatz as well as the orbit and multiangle ansätze. We are able to fully characterize the Lie algebras of the multiangle ansatz across arbitrary connected graphs, finding that they only fall into one of just six families. Aside from the cycle and path graphs, the Lie dimensions for every graph are exponentially large in the system size, meaning that multiangle ansätze are extremely prone to exhibiting barren plateaus. Then, a similar quasi-graph-independent Lie-algebraic characterization beyond the multiangle ansatz is impeded as the circuit exhibits additional “hidden” symmetries besides those naturally arising from a certain parity-superselection operator and all automorphisms of the considered graph. Disregarding the “hidden” symmetries, we can upper bound the dimensions of the orbit and the standard Lie algebras, and the dimensions of the associated invariant subspaces are determined via explicit character formulas. To finish, we conjecture that (for most graphs) the standard Lie algebras have only components that are either exponential or that grow, at most, polynomially with the system size. This would imply that the QAOA is either prone to barren plateaus or classically simulable. More generally, our work provides a symmetry framework and tools to analyze any desired variational quantum algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

On Projection of Safe Operation for Grid-Following Inverters - Grid Parameter Estimation

This work investigates the projection of safe operation for grid-following inverters using a reference model. This work applies recursive-least square and model reference adaptive estimation techniques to estimate the unknown grid parameters. The estimated grid parameters are used in a dynamic reference model to project the safe operation of the inverter. This work also demonstrates that the controller nonlinearity and parameter variations can cause unsafe operations, such as unexpected instability at low power levels, namely hidden mode of instability. This instability issue in the nominal operating range may occur for given control parameters in a weak-grid condition. The dynamic reference model for PQ-controlled inverters is applied to analyze this instability issue using the estimated grid parameters. Both adaptive identification techniques can estimate the unknown parameters accurately, and the dynamic reference model can safeguard inverters from an unsafe operation, i.e., hidden instability, by examining the incoming new power setpoints before engaging them to the local controller. The findings are experimentally verified using a small-scale two-level 208 V, 5 kVA inverter feeding a 12 kW NHR 9410 power grid emulator.

Hossen, Tareq↗

DOE OSTI2020

Net Load Redistribution Attacks on Nodal Voltage Magnitude Estimation in AC Distribution Networks

A high penetration level of smart devices and communication networks increases the threat of cyber-attacks in the distribution system. In this paper, we model a hidden, coordinated, net load redistribution attack (NLRA) in an AC distribution system. Based on local information of an attack region, the attacker’s goal is to create violations in nodal voltage magnitude estimation. Acting as a system operator equipped with global AC state estimation and bad data detection, we validate the stealthiness of the hidden NLRA in multiple attack cases. Simulation results on a modified PG&E 69-node distribution system show the validity of the proposed NLRA. The influence of NLRA on the distribution system is assessed and the impact of attack regions, attack timing, and system observability is also revealed.

Zhang, Hang↗