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At least 775 records · Page 43

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DOE OSTI2023

Asynchronous domain dynamics and equilibration in layered oxide battery cathode

To improve lithium-ion battery technology, it is essential to probe and comprehend the microscopic dynamic processes that occur in a real-world composite electrode under operating conditions. The primary and secondary particles are the structural building blocks of battery cathode electrodes. Their dynamic inconsistency has profound but not well-understood impacts. In this research, we combine operando coherent multi-crystal diffraction and optical microscopy to examine the chemical dynamics in local domains of layered oxide cathode. Our results not only pinpoint the asynchronicity of the lithium (de)intercalation at the sub-particle level, but also reveal sophisticated diffusion kinetics and reaction patterns, involving various localized processes, e.g., chemical onset, reaction front propagation, domains equilibration, particle deformation and motion. These observations shed new lights onto the activation and degradation mechanisms of state-of-the-art battery cathode materials.

25 ENERGY STORAGE↗

DOE OSTI2024

Room-temperature sub-100 nm Néel-type skyrmions in non-stoichiometric van der Waals ferromagnet Fe 3-$x$ GaTe 2 with ultrafast laser writability

Realizing room-temperature magnetic skyrmions in two-dimensional van der Waals ferromagnets offers unparalleled prospects for future spintronic applications. However, due to the intrinsic spin fluctuations that suppress atomic long-range magnetic order and the inherent inversion crystal symmetry that excludes the presence of the Dzyaloshinskii-Moriya interaction, achieving room-temperature skyrmions in 2D magnets remains a formidable challenge. In this study, we target room-temperature 2D magnet Fe 3 GaTe 2 and unveil that the introduction of iron-deficient into this compound enables spatial inversion symmetry breaking, thus inducing a significant Dzyaloshinskii-Moriya interaction that brings about room-temperature Néel-type skyrmions with unprecedentedly small size. To further enhance the practical applications of this finding, we employ a homemade in-situ optical Lorentz transmission electron microscopy to demonstrate ultrafast writing of skyrmions in Fe 3-$x$ GaTe 2 using a single femtosecond laser pulse. Our results manifest the Fe 3-$x$ GaTe 2 as a promising building block for realizing skyrmion-based magneto-optical functionalities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Structural diversity and clustering of bacterial flagellar outer domains

Supercoiled flagellar filaments function as mechanical propellers within the bacterial flagellum complex, playing a crucial role in motility. Flagellin, the building block of the filament, features a conserved inner D0/D1 core domain across different bacterial species. In contrast, approximately half of the flagellins possess additional, highly divergent outer domain(s), suggesting varied functional potential. In this study, we report atomic structures of flagellar filaments from three distinct bacterial species: Cupriavidus gilardii , Stenotrophomonas maltophilia , and Geovibrio thiophilus . Our findings reveal that the flagella from the facultative anaerobic G. thiophilus possesses a significantly more negatively charged surface, potentially enabling adhesion to positively charged minerals. Furthermore, we analyze all AlphaFold predicted structures for annotated bacterial flagellins, categorizing the flagellin outer domains into 682 structural clusters. This classification provides insights into the prevalence and experimental verification of these outer domains. Remarkably, two of the flagellar structures reported herein belong to a distinct cluster, indicating additional opportunities on the study of the functional diversity of flagellar outer domains. Our findings underscore the complexity of bacterial flagellins and open up possibilities for future studies into their varied roles beyond motility.

Science & Technology - Other Topics↗

DOE OSTI2025

Deciphering metabolic differentiation during Bacillus subtilis sporulation

Abstract The bacteriumBacillus subtilisundergoes asymmetric cell division during sporulation, producing a mother cell and a smaller forespore connected by the SpoIIQ-SpoIIIA (or Q-A) channel. The two cells differentiate metabolically, and the forespore becomes dependent on the mother cell for essential building blocks. Here, we investigate the metabolic interactions between mother cell and forespore using genome-scale metabolic and expression models as well as experiments. Our results indicate that nucleotides are synthesized in the mother cell and transported in the form of nucleoside di- or tri-phosphates to the forespore via the Q-A channel. However, if the Q-A channel is inactivated later in sporulation, then glycolytic enzymes can form an ATP and NADH shuttle, providing the forespore with energy and reducing power. Our integrated in silico and in vivo approach sheds light into the intricate metabolic interactions underlying cell differentiation inB. subtilis, and provides a foundation for future studies of metabolic differentiation.

Science & Technology - Other Topics↗

DOE OSTI2025

Realizing mechanical frustration at the nanoscale using DNA origami

Structural designs inspired by physical and biological systems have been previously utilized to develop mechanical metamaterials with enhanced properties based on clever geometric arrangement of constituent building blocks. Here, we use the DNA origami method to realize a nanoscale metastructure exhibiting mechanical frustration, a counterpart of the well-known phenomenon of magnetic frustration. By selectively actuating reconfigurable struts, it adopts either frustrated or non-frustrated states, each characterized by distinct free energy profiles. While the non-frustrated state distributes the strain homogeneously, the frustrated mode concentrates it at a specific location. Molecular dynamics simulations reconcile the contrasting behaviors and provide insights into underlying mechanics. We explore the design space further by tailoring responses through structural modifications. Our work combines programmable DNA self-assembly with mechanical design principles to overcome engineering limitations encountered at the macroscale to design dynamic, deformable nanostructures with potential applications in elastic energy storage, nanomechanical computation, and allosteric mechanisms in DNA-based nanomachinery.

DNA nanostructures↗

DOE OSTI2025

Scalable 3D reconstruction for X-ray single particle imaging with online machine learning

X-ray free-electron lasers offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. Notably, high-repetition-rate free-electron lasers enable single particle imaging, where individual, weakly scattering biomolecules are imaged under near-physiological conditions with the opportunity to access fleeting states that cannot be captured in cryogenic or crystallized conditions. Existing X-ray single particle reconstruction algorithms, which estimate the particle orientation for each image independently, are slow and memory-intensive when handling the massive datasets generated by emerging free-electron lasers. Here, we introduce X-RAI (X-Ray single particle imaging with Amortized Inference), an online reconstruction framework that estimates the structure of 3D macromolecules from large X-ray single particle datasets. X-RAI consists of a convolutional encoder, which amortizes pose estimation over large datasets, as well as a physics-based decoder, which employs an implicit neural representation to enable high-quality 3D reconstruction in an end-to-end, self-supervised manner. We demonstrate that X-RAI achieves state-of-the-art performance for small-scale datasets in simulation and challenging experimental settings and demonstrate its unprecedented ability to process large datasets containing millions of diffraction images in an online fashion. These abilities signify a paradigm shift in X-ray single particle imaging towards real-time reconstruction.

Computer science↗

DOE OSTI2025

Temperature measurement of Quark-Gluon plasma at different stages

In a Quark-Gluon Plasma (QGP), the fundamental building blocks of matter, quarks and gluons, are under extreme conditions of temperature and density. A QGP could exist in the early stages of the Universe, and in various objects and events in the cosmos. The thermodynamic and hydrodynamic properties of the QGP are described by Quantum Chromodynamics (QCD) and can be studied in heavy-ion collisions. Despite being a key thermodynamic parameter, the QGP temperature is still poorly known. Thermal lepton pairs (e + e − and μ + μ − ) are ideal penetrating probes of the true temperature of the emitting source, since their invariant-mass spectra suffer neither from strong final-state interactions nor from blue-shift effects due to rapid expansion. Here we measure the QGP temperature using thermal e+e− production at the Relativistic Heavy Ion Collider (RHIC). The average temperature from the low-mass region (in-medium ρ 0 vector-meson dominant) is (2.01 ± 0.23) × 10 12 K, consistent with the chemical freeze-out temperature from statistical models and the phase transition temperature from Lattice QCD. The average temperature from the intermediate mass region (above the ρ0 mass, QGP dominant) is significantly higher at (3.25 ± 0.60) × 10 12 K. This work provides essential experimental thermodynamic measurements to map out the QCD phase diagram and understand the properties of matter under extreme conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Nonlinear thermodynamic computing out of equilibrium

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.

Whitelam, Stephen [Lawrence Berkeley National Labo↗

DOE OSTI2026

Mesoporous peptide frameworks engineered from crystallizable collagen-mimetic peptide amphiphiles

The rational design of porous frameworks with tunable pore dimensions and chemical functionalities is a critical step toward their implementation in diverse applications. While traditional porous materials are typically constructed from abiotic components, there is increasing interest in employing biologically derived building blocks (e.g., peptides and proteins) that offer unmatched structural and functional diversity. Here, we report the construction of crystalline mesoporous frameworks that are self-assembled from amphiphilic collagen-mimetic peptides. Comprehensive structural characterization via microscopy, spectroscopy, and computational techniques provides insights into the assembly packing model, in which hexagonally packed channels are interconnected by antiparallel-aligned collagen triple helices via hydrophobic and electrostatic interactions. Lastly, we demonstrate the functional potential of aCMP frameworks through the encapsulation of various molecular guests, including doxorubicin, an anti-cancer drug. Overall, this work establishes a class of mesoporous frameworks, derived from synthetically engineerable peptide conjugates, marking a significant step forward in broadening the architectural scope and application potential of peptide-based materials.

Perez, Anthony R↗

DOE OSTI2026

Ultranarrow bright single-photon emitters in diamond with strong broadband phonon decoupling

Single-photon emitters are fundamental building blocks for quantum information processing, communication and sensing. However, unwanted interactions with bulk phonons in their host environment strongly limit their coherence and controllability. We report single color centers in nanodiamonds that are strongly and comprehensively decoupled from the bulk phononic environment. The color centers feature record-narrow linewidths down to 0.3 nm at room temperature and stable, bright emission, exceeding 10 Mcps in saturation. Notably, the bulk phonon sideband is almost entirely suppressed, revealing the presence of a single localized vibrational mode outside the diamond phonon band. Our observations and simulations point towards a unique mechanism for phonon decoupling in common wide-gap materials, based on a strongly radiative orbital transition coupled to a localized vibrational mode. The new color center enables qualitatively higher performance for applications in quantum networks and nanoscale sensing, and the exploration of new physical resources associated with vibrational states.

Sahoo, Swetapadma [ORNL]↗

DOE OSTI2023

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

AI-assisted rapid crystal structure generation towards a target local environment

In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.

Ridwan, Osman Goni [University of North Carolina a↗

DOE OSTI2021

An artificial spiking quantum neuron

Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.

Kristensen, Lasse Bjørn (ORCID:0000000239398170)↗

DOE OSTI2022

Reconfigurable quantum phononic circuits via piezo-acoustomechanical interactions

Abstract We show that piezoelectric strain actuation of acoustomechanical interactions can produce large phase velocity changes in an existing quantum phononic platform: aluminum nitride on suspended silicon. Using finite element analysis, we demonstrate a piezo-acoustomechanical phase shifter waveguide capable of producing ± π phase shifts for GHz frequency phonons in 10s of μm with 10s of volts applied. Then, using the phase shifter as a building block, we demonstrate several phononic integrated circuit elements useful for quantum information processing. In particular, we show how to construct programmable multi-mode interferometers for linear phononic processing and a dynamically reconfigurable phononic memory that can switch between an ultra-long-lifetime state and a state strongly coupled to its bus waveguide. From the master equation for the full open quantum system of the reconfigurable phononic memory, we show that it is possible to perform read and write operations with over 90% quantum state transfer fidelity for an exponentially decaying pulse.

Taylor, Jeffrey C. (ORCID:0000000215982436)↗

DOE OSTI2023

Programmable photonic integrated meshes for modular generation of optical entanglement links

Abstract Large-scale generation of quantum entanglement between individually controllable qubits is at the core of quantum computing, communications, and sensing. Modular architectures of remotely-connected quantum technologies have been proposed for a variety of physical qubits, with demonstrations reported in atomic and all-photonic systems. However, an open challenge in these architectures lies in constructing high-speed and high-fidelity reconfigurable photonic networks for optically-heralded entanglement among target qubits. Here we introduce a programmable photonic integrated circuit (PIC), realized in a piezo-actuated silicon nitride (SiN)-in-oxide CMOS-compatible process, that implements an N × N Mach–Zehnder mesh (MZM) capable of high-speed execution of linear optical transformations. The visible-spectrum photonic integrated mesh is programmed to generate optical connectivity on up to N = 8 inputs for a range of optically-heralded entanglement protocols. In particular, we experimentally demonstrated optical connections between 16 independent pairwise mode couplings through the MZM, with optical transformation fidelities averaging 0.991 ± 0.0063. The PIC’s reconfigurable optical connectivity suffices for the production of 8-qubit resource states as building blocks of larger topological cluster states for quantum computing. Our programmable PIC platform enables the fast and scalable optical switching technology necessary for network-based quantum information processors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Fault-tolerant operation and materials science with neutral atom logical qubits

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell state preparation (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C 4 ). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 demonstrating results consistent with fault tolerance. In light of recent advances on applying concatenated C 4 /C 6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Majorana modes with side features in magnet-superconductor hybrid systems

Magnet-superconductor hybrid (MSH) systems represent promising platforms to host Majorana zero modes (MZMs), the elemental building blocks for fault-tolerant quantum computers. Theoretical description of such MSH structures is mostly based on simplified models, not accounting for the complexity of real materials. Here, based on density functional theory, we derive a superconducting 80-band model to study an MSH system consisting of a magnetic manganese chain on the s wave superconductor niobium. For a wide range of values of the superconducting order parameter, the system is a topological superconductor, with MZMs exhibiting non-universal spatial patterns and a drastic accumulation of spectral weight on both sides along the magnetic chain. These side feature states can be explained by an effective model which is guided by the ab initio results. Performing scanning tunneling spectroscopy experiments on the same system, we observe a spatial structure in the low-energy local density of states that is consistent with the theoretical findings. Our results open a first-principle approach to the discovery of topological superconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Real-space chirality from crystalline topological defects in the Kitaev spin liquid

We show that certain crystalline topological defects in the gapless Kitaev honeycomb spin liquid model generate a chirality and Majorana fermion orbital magnetization that depends in a universal manner on their emergent flux. Focusing on 5–7 dislocations as building blocks, consisting of pentagon and heptagon disclinations, we identify the Kitaev bond label configurations that preserve solvability. By computing two formulations of local markers M(r) we find that the 5 and 7 lattice defects generate a real-space contribution to Chern number and an associated Majorana fermion orbital magnetization proportional to M(r). The sign of the M(r) contribution from each 5/7 defect, i.e. its q M = ± 1 chirality, is determined by the defect Frank angle sign F and emergent gauge field flux W = ± i through the expression q M = − iFW. Remarkably, though lattice curvature and torsion can interplay with the surrounding gapless background to modify the profile of M(r), its sign q M is determined locally, implying that crystalline defects in the Kitaev spin liquid can generate a robust and observable chirality.

Magnetic properties and materials↗