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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Universal linear intensity transformations using spatially incoherent diffractive processors

Abstract Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number ( N ) of optimizable phase-only diffractive features is ≥~2 N i N o , where N i and N o refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function ( H ) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H ( m , n ; m ′, n ′) = | h ( m , n ; m ′, n ′)| 2 , where h is the spatially coherent point spread function of the same diffractive network, and ( m , n ) and ( m ′, n ′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2 N i N o . We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental demonstration of continuous quantum error correction

The storage and processing of quantum information are susceptible to external noise, resulting in computational errors. A powerful method to suppress these effects is quantum error correction. Typically, quantum error correction is executed in discrete rounds, using entangling gates and projective measurement on ancillary qubits to complete each round of error correction. Here we use direct parity measurements to implement a continuous quantum bit-flip correction code in a resource-efficient manner, eliminating entangling gates, ancillary qubits, and their associated errors. An FPGA controller actively corrects errors as they are detected, achieving an average bit-flip detection efficiency of up to 91%. Furthermore, the protocol increases the relaxation time of the protected logical qubit by a factor of 2.7 over the relaxation times of the bare comprising qubits. Our results showcase resource-efficient stabilizer measurements in a multi-qubit architecture and demonstrate how continuous error correction codes can address challenges in realizing a fault-tolerant system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Evolution of neuronal cell classes and types in the vertebrate retina

The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs1. Retinal cell types may have evolved to accommodate these varied needs, but this has not been systematically studied. Here we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a bird, a reptile, a teleost fish and a lamprey. We found high molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells (RGCs) and Müller glia), with transcriptomic variation across species related to evolutionary distance. Major subclasses were also conserved, whereas variation among cell types within classes or subclasses was more pronounced. However, an integrative analysis revealed that numerous cell types are shared across species, based on conserved gene expression programmes that are likely to trace back to an early ancestral vertebrate. The degree of variation among cell types increased from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified rodent orthologues of midget RGCs, which comprise more than 80% of RGCs in the human retina, subserve high-acuity vision, and were previously believed to be restricted to primates. By contrast, the mouse orthologues have large receptive fields and comprise around 2% of mouse RGCs. Projections of both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but are descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Electrical switching of a p-wave magnet

Magnetic states with zero magnetization but non-relativistic spin splitting are outstanding candidates for the next generation of spintronic devices. Their electronvolt (eV)-scale spin splitting, ultrafast spin dynamics and nearly vanishing stray fields make them particularly promising for several applications. A variety of such magnetic states with non-trivial spin textures have been identified recently, including even-parity d-wave, g-wave or i-wave altermagnets and odd-parity p-wave magnets. Achieving voltage-based control of the non-uniform spin polarization of these magnetic states is of great interest for realizing energy-efficient and compact devices for information storage and processing. Spin-spiral type II multiferroics are optimal candidates for such voltage-based control, as they exhibit an inversion-symmetry-breaking magnetic order that directly induces ferroelectric polarization, allowing for symmetry-protected cross-control between spin chirality and polar order. Here we combine photocurrent measurements, first-principles calculations and group-theory analysis to provide direct evidence that the spin polarization of the spin-spiral type II multiferroic NiI 2 exhibits odd-parity character connected to the spiral chirality. The symmetry-protected coupling between chirality and polar order enables electrical control of a primarily non-relativistic spin polarization. Our findings represent an observation of p-wave magnetism in a spin-spiral type II multiferroic, which may lead to the development of voltage-based switching of non-relativistic spin polarization in compensated magnets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Kelvin probe force microscopy under ambient conditions

Kelvin probe force microscopy (KPFM) is a technique derived from atomic force microscopy that provides maps of surface potential or work function differences across material systems, with nanometre-scale resolution. KPFM is a useful tool for investigating electrical phenomena such as dipole orientation, interfacial charge transfer, charge accumulation, band bending and doping levels. This Primer aims to provide an overview of typical ambient-condition KPFM measurements, covering their underlying principles, experimental implementations and wide-ranging applications. Key KPFM variants, including amplitude and frequency modulation, heterodyne detection schemes and innovative open loop and pulsed force techniques, are discussed, with practical guidance on optimizing signal acquisition and reducing errors. Specialized approaches, such as time-resolved KPFM and multimodal KPFM, are discussed for their ability to capture dynamic charge processes and chemical information, respectively. Here, we highlight recent advances in KPFM applications, spanning metal alloys, soft matter, ferroelectrics, photovoltaics and 2D materials, showcasing its versatility across research domains. By addressing current limitations and identifying future opportunities, this Primer underscores the transformative potential of KPFM in advancing the understanding of nanoscale electrical phenomena.

Zahmatkeshsaredorahi, Amirhossein [Lehigh Univ., B↗
DOE OSTI2024

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. Here, in this work, we present a Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the state predictive information bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features.

Zou, Ziyue↗
DOE OSTI2021

WLCG Token Usage and Discovery

Since 2017, the Worldwide LHC Computing Grid (WLCG) has been working towards enabling token based authentication and authorisation throughout its entire middleware stack. Following the publication of the WLCG Common JSON Web Token (JWT) Schema v1.0 [1] in 2019, middleware developers have been able to enhance their services to consume and validate the JWT-based [2] OAuth2.0 [3] tokens and process the authorization information they convey. Complex scenarios, involving multiple delegation steps and command line flows, are a key challenge to be addressed in order for the system to be fully operational. This paper expands on the anticipated token based workflows, with a particular focus on local storage of tokens and their discovery by services. The authors include a walk-through of this token flow in the RUCIO managed data-transfer scenario, including delegation to FTS and authorised access to storage elements. Next steps are presented, including the current target of submitting production jobs authorised by Tokens within 2021.

Bockelman, Brian↗
DOE OSTI2022

Supervised learning and the finite-temperature string method for computing committor functions and reaction rates

A central object in the computational studies of rare events is the committor function. Though costly to compute, the committor function encodes complete mechanistic information of the processes involving rare events, including reaction rates and transition-state ensembles. Under the framework of transition path theory, Rotskoff et al. [Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference, Proceedings of Machine Learning Research (PLMR, 2022), Vol. 145, pp. 757–780] proposes an algorithm where a feedback loop couples a neural network that models the committor function with importance sampling, mainly umbrella sampling, which collects data needed for adaptive training. Here, in this work, we show additional modifications are needed to improve the accuracy of the algorithm. The first modification adds elements of supervised learning, which allows the neural network to improve its prediction by fitting to sample-mean estimates of committor values obtained from short molecular dynamics trajectories. The second modification replaces the committor-based umbrella sampling with the finite-temperature string (FTS) method, which enables homogeneous sampling in regions where transition pathways are located. We test our modifications on low-dimensional systems with non-convex potential energy where reference solutions can be found via analytical or finite element methods, and show how combining supervised learning and the FTS method yields accurate computation of committor functions and reaction rates. We also provide an error analysis for algorithms that use the FTS method, using which reaction rates can be accurately estimated during training with a small number of samples. The methods are then applied to a molecular system in which no reference solution is known, where accurate computations of committor functions and reaction rates can still be obtained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling

Significance Soil organic matter contains a large fraction of global C whose fate is largely governed by soil microbial communities. Global C-cycle models can be improved using information about microbial processes in soil, but the inner workings of soil communities remain largely hidden from view. We used stable isotopes to characterize the guild structure of soil bacteria based on how they assimilated C from different sources. Guild structure corresponded with growth dynamics and rrn copy number. We also show that this guild structure explains microbial biogeographical patterns at continental and global scales. These results suggest that microbial contributions to soil C cycling are largely governed by an anabolic process that governs energy allocation to growth, competition, and survival.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Conceptual Design of the Transformational Challenge Reactor

The Transformational Challenge Reactor is a 3-MW(thermal) helium-cooled experimental nuclear reactor designed using an additive manufacturing–informed agile design process. This design process leverages rapid prototyping and advanced materials from emerging additive manufacturing technologies, key characteristics that enable rapid design maturation. The resulting core design incorporates a blend of advanced reactor technologies into an intermediate-spectrum microreactor, including conventionally manufactured tristructural isotropic (TRISO) fuel particles in an advanced manufactured SiC fuel element and a solid yttrium hydride moderator encapsulated in steel. Matured during the design effort, these technologies are incorporated with additively manufactured steel support and fluidic structures to form a 75-cm-outer-diameter cylindrical active core region. Below and above the active core region are axial SiC reflectors, which are housed inside the reactor pressure vessel. The reactor is controlled with an annular shroud actuated external to the pressure vessel in the gap between the pressure vessel and a steel radial reflector. A safety rod is at the center of the core to shut down the reactor when necessary. Helium pressurized at 5 MPa is forced into the pressure vessel below the core and around the core to the top plenum before it is forced down through the axial reflectors and the active core region. The primary pressurized helium loop is operated up to 500°C and includes the pressure vessel, the circulator, and the hot side of a helium-to-air heat exchanger. The secondary loop rejects all heat from the primary loop to ambient air through a heat exchanger. A vented temporary confinement building contains the entire primary loop, with penetrations for a stack, cooling, and the secondary ambient air loop. Finally, this is the first advanced nuclear microreactor designed using additive manufacturing technologies, demonstrating their applicability in an accelerated advanced design process.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Niche Partitioning of Microbial Communities at an Ancient Vitrified Hillfort: Implications for Vitrified Radioactive Waste Disposal

A pre-Viking era vitrified hillfort, Broborg, provides a habitat analogue for disposed radioactive waste glass and shows strong niche partitioning among the organisms involved in glass alteration. Microbes cannot be eliminated from radioactive waste disposal facilities and the consequences of bio-colonization must be understood. We use Broborg as a model system to inform what microbial processes might influence long-term radioactive waste glass durability by examining anthropogenic glass that has been subjected to bio-colonization for over 1,500 years. Scanning electron microscopy (SEM) images reveal the surficial biofilm structure, and chemical/mineralogy analysis in combination with deoxyribonucleic acid (DNA) sequencing of samples from the vitrified substrate, the adjacent soil, and the general topsoil provide insight into niche partitioning. The ancient glass niche supports a unique microbial community of bacteria, fungi, and protists that manifests the species response to local geochemical and mineralogical conditions. Communities from the geochemical niche associated with the glass are distinct and less diverse than soil communities. The microbiome of the glass and adjacent soil are dominated by lichens, lichen-associated microorganisms, and other epilithic, endolithic, and epigeic organisms. Pseudomonads dominate the prokaryotic communities on the vitrified material, but not the adjacent soil. In contrast, the general topsoil communities are enriched in plant rhizosphere organisms. Taxa associated with vitrification have bio-corrosive properties that could be detrimental to glass durability, including silicate mineral dissolution, extraction of essential elements, secretion of geochemically reactive organic acids, and dissolution induced by improved water retention. However, these stable long-term biofilms also possess a homeostatic function that could limit glass alteration. Overall, this study demonstrates the potential impacts that microbial colonization and niche partitioning can have on glass alteration and subsequent release of radionuclides from a disposal facility for vitrified radioactive waste.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine Learning of Performance Space Mapping for the DPD Simulation of Drug Delivery to Endothelial Cells

Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling methane dynamics in three wetlands in Northeastern China by using the CLM-Microbe model

Wetlands account for up to 70% of the natural source of methane (CH 4 ) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH 4 in wetlands. Therefore, simulating microbial mechanisms of CH 4 production and consumptionwould improve the predictability of CH 4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH 4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH 4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH 4 flux across sites. Marsh wetlands had higher CH 4 flux than mountain peatlands. Ebullition dominated the CH 4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH 4 flux, making all wetlands a CH 4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH 4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH 4 dynamics under the changing environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Understanding attosecond streaking

Abstract This tutorial provides an overview on the theory of attosecond streaking, a pump-probe scheme to extract timing information of ionization processes that has been widely used in the past decade. Emphasis is put on the origin of the Coulomb-laser-coupling (CLC) term, which is crucial in the interpretation of streaking delays. Having gained a proper understanding of how the CLC terms in various publications relate to each other, we will be able to analyze in which regime the streaking delay can be split into a measurement-induced CLC term and a ‘pure’ ionization delay and under which conditions this splitting may break down. Thus we address the long-standing question of the validity of the widely applied interpretation of the streaking delay as a sum of the CLC term and a ‘pure’ ionization delay.

Physics↗
DOE OSTI2023

Roadmap on spatiotemporal light fields

Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents a major goal of the everlasting pursue of ultra-fast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally, spatiotemporal light pulses are always treated as spatiotemporally separable wave packet as solution of the Maxwell’s equations. In the past decade, however, more generalized forms of spatiotemporally nonseparable solution started to emerge with growing importance for their striking physical effects. This roadmap intends to highlight the recent advances in the creation and control of increasingly complex spatiotemporally sculptured pulses, from spatiotemporally separable to complex nonseparable states, with diverse geometric and topological structures, presenting a bird’s eye viewpoint on the zoology of spatiotemporal light fields and the outlook of future trends and open challenges.

42 ENGINEERING↗
DOE OSTI2020

Electronic structure calculations of twisted multi-layer graphene superlattices

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational discovery of two-dimensional rare-earth iodides: promising ferrovalley materials for valleytronics

Two-dimensional Ferrovalley materials with intrinsic valley polarization are rare but highly promising for valley-based nonvolatile random access memory and valley filter devices. These ferromagnetic materials exhibit valleys at or near the Fermi level with intrinsic magnetism. The strong coupling between magnetism and spin–orbit coupling induces intrinsic valley polarization. Using Kinetically Limited Minimization, an unconstrained crystal structure prediction algorithm, and prototype sampling based on first-principles calculations, we have discovered new Ferrovalley materials, rare-earth iodides RI 2 , where R is a rare-earth element belonging to Sc, Y, or La-Lu, and I is Iodine. The rare-earth iodides are layered and demonstrate either 2H, 1T, or 1T d phase as the ground state in bulk, analogous to transition metal dichalcogenides (TMDCs). The calculated exfoliation energy of monolayers (MLs) is comparable to that of graphene and TMDCs, suggesting possible experimental synthesis. The MLs in the 2H phase exhibit ferromagnetism due to unpaired electrons in d and f orbitals. Throughout the rare-earth series, d bands have valley polarization at K and $\overline{K}$ points in the Brillouin zone in the vicinity of the Fermi level. Large intrinsic valley polarization in the range of 15–143 meV without external stimuli is observed in these Ferrovalley materials, which can be enhanced further by applying an in-plane bi-axial strain. These valleys can selectively be probed and manipulated for information storage and processing, potentially offering superior performance beyond conventional electronics and spintronics. Here we further show that the 2H ferromagnetic phase of RI 2 MLs possesses non-zero Berry curvature and exhibits anomalous valley Hall effect with considerable anomalous Hall conductivity. Our work will incite exploratory synthesis of the predicted Ferrovalley materials and their application in valleytronics and beyond.

2D materials↗