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At least 451 records · Page 25

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DOE OSTI2025

SwinCell: a 3D transformer and flow-based framework for improved cell segmentation

Segmentation of three-dimensional (3D) cellular images is fundamental for studying and understanding cell structure and function. However, 3D cellular segmentation is challenging, particularly for dense cells and tissues. This challenge arises mainly from the complex contextual information within 3D images, anisotropic properties, and the sensitivity to internal cellular structures, which often lead to incorrect segmentation. In this work, we introduce SwinCell, a 3D transformer-based framework that leverages Swin-transformer to predict flow and differentiate individual cell instances. We demonstrate SwinCell’s utility in the segmentation of nuclei, colon tissue cells, and densely cultured cells. SwinCell strikes a balance between maintaining detailed local feature recognition and understanding broader contextual information. Through extensive testing with both public and in-house 3D cell imaging datasets, SwinCell shows utility in segmenting dense cells, making it a valuable tool for 3D segmentation in cellular analysis that could expedite research in cell biology and tissue engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Crack mitigation in additively manufactured AlCrFe 2 Ni 2 high-entropy alloys through engineering phase transformation pathway

The far-from-equilibrium solidification during additive manufacturing often creates large residual stresses that induce solid-state cracking. Here we present a strategy to suppress solid-state cracking in an additively manufactured AlCrFe 2 Ni 2 high-entropy alloy via engineering phase transformation pathway. We investigate the solidification microstructures formed during laser powder-bed fusion and directed energy deposition, encompassing a broad range of cooling rates. At high cooling rates (10 4 –10 6 K/s), we observe a single-phase BCC/B2 microstructure that is susceptible to solid-state cracking. At low cooling rates (10 2 –10 4 K/s), FCC phase precipitates out from the BCC/B2 matrix, resulting in enhanced ductility (~10 %) and resistance to solid-state cracking. Site-specific residual stress/strain analysis reveals that the ductile FCC phase can largely accommodate residual stresses, a feature which helps relieve residual strains within the BCC/B2 phase to prevent cracking. Our work underscores the value of exploiting the toolbox of phase transformation pathway engineering for material design during additive manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Stress-dependent χ phase transformation in a Ni-based superalloy

The ongoing push to elevate operating temperatures in aerospace gas turbine engines – driven by goals of enhanced fuel efficiency and reduced CO 2 emissions – mandates advancements in the creep resistance of Ni- and Co-based superalloys, which are integral for critical engine components. This study elucidates the role of stress assisted localized phase transformations in the creep properties of these alloys. By leveraging chemo-mechanical coupling, self-healing γ′ precipitates are designed to immobilize planar defects, thereby increasing creep resistance. Employing advanced characterization techniques such as high-resolution Scanning Transmission Electron Microscopy (HR-STEM), in conjunction with atomistic simulations and thermodynamic calculations, novel deformation pathways facilitated by χ local phase transformation (LPT) strengthening have been uncovered; notably, the formation of χ nano-laths through microtwinning and superlattice intrinsic stacking fault (SISF) shearing. This study highlights critical insights into the compositional boundaries necessary for optimizing LPT strengthening while avoiding deleterious bulk formation of η/χ phases. These advancements will guide the design of new alloys maximizing high-temperature creep strength for advanced aerospace applications.

Egan, Ashton J. [Friedrich-Alexander University Er↗

DOE OSTI2025

Atomic-scale insights into topotactic transformations in an extra-large-pore zeolite using time-resolved 3D electron diffraction

Understanding the atomic-scale structural dynamics of phase transformations is crucial for developing materials and tailoring their properties. However, many materials are obtained as polycrystalline powders with large unit cells and/or complex structures, making it challenging to investigate detailed structural changes using conventional X-ray diffraction techniques. Here we employ time-resolved three-dimensional electron diffraction to reveal the topotactic reactions and transformations that convert the extra-large-pore silicate zeolite ECNU-45 into ECNU-46. ECNU-45 features three-dimensional interconnecting 24 × 10 × 10-ring channels, while ECNU-46 consists of one-dimensional 24-ring channels connected to 10-ring pockets. ECNU-45 and ECNU-46 are both examples of pure silicate zeolites with pore openings larger than 22-ring. Our findings indicate changes at six distinct tetrahedral silicon sites, involving atom displacement, addition and removal of framework atoms through bond breakage and formation. This work presents the synthesis of zeolites and also provides atomic-level insights into the dynamic processes of topotactic reactions. Our results have implications for advancing materials engineering and understanding complex solid-state reactions at an atomic scale.

Inorganic chemistry↗

DOE OSTI2026

Kinetic-controlled transformations of group-III arsenide nanocubes

Tracking the structural evolution of colloidal nanocrystals (NCs) facilitates the mechanistic studies of their materials chemistry. NC engineering via phase transformation reveals the chemical and physical determinants that drive lattice-scale dynamic processes such as cation exchange. Here, in this study, we employed NCs to demonstrate the cation exchange process from Cu 3 As to InAs and GaAs within nanocubes. The symmetry conversion in unit cells from cubic Cu 3 As to hexagonal InAs and GaAs can be described using a schematic cellular automaton model, which suggests a simplified cube-to-sphere transition. The strong covalent characteristics of III–V materials highlight the kinetic control that navigates the tailorable transformation through either an isotropic trajectory, leading to hollow structures, or a topotaxial trajectory, with abundant stacking faults. The reconstruction of complex covalent bonds is envisioned as the foundation for the synthesis of NCs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Metal ion dopant-induced famatinite to chalcostibite phase transformation of copper antimony sulphide colloidal nanostructures: effect on photophysical and pseudocapacitance properties

Here, the strategic doping of transition metal ions into copper antimony sulphide (CAS) semiconducting nanostructures can significantly influence their photophysical and pseudocapacitive properties, for which there are few reports and therefore is the focus of this study. Accordingly, highly crystalline metal ion (Mn 2+ , Fe 2+ , Co 2+ , Ni 2+ and Zn 2+ ) doped off-stoichiometric copper rich/poor, antimony-rich/poor, and sulphur-poor CAS nanostructures (10–23 nm) were grown via colloidal (hot-injection) synthesis using metal diethyldithiocarbamate precursors. Importantly, metal ion doping significantly influences the structure and composition of the off-stoichiometric CAS nanostructures. Data from powder X-ray diffraction, Raman spectroscopy, high-resolution scanning/transmission electron microscopy, and energy dispersive X-ray spectroscopy confirm that the heavier metal ion dopants induce a novel phase transformation from famatinite (fCAS) to chalcostibite (cCAS) nanostructures. The influence of the metal ion dopants is also observed in the optical properties of as-synthesized nanostructures. This involves blue-shifted ultraviolet-visible absorption, reduced Urbach tailing, and tunable bandgaps between 2.17 and 2.38 eV. Also, the doped nanostructures display broad visible-near infrared photoluminescence via a triple radiative pathway with relatively short decay lifetimes between 0.2 and 6.1 ns. This is mediated by electronic transitions involving intrinsic (copper/antimony/sulphur interstitial) and extrinsic (metal ion interstitial) defect states. Additionally, electrodes prepared from Mn 2+ -doped fCAS nanostructures show enhanced pseudocapacitance via Na+ surface adsorption and intercalation relative to undoped fCAS electrodes, while Zn 2+ -doped cCAS electrodes exhibit pseudocapacitance via a combination of Na+ surface adsorption, intercalation, and redox reactions. These electrodes exhibit reduced charge transfer resistance, improved electronic conductivity and notably enhanced specific capacitance (∼222 F g −1 ), and charge transport, as measured in 1 M Na 2 SO 4 electrolyte via cyclic voltammetry and electrochemical impedance spectroscopy. To this end, the discovery of the metal ion-induced phase transformation presents a new avenue for optimizing the functional properties of fCAS and cCAS nanostructures, highlighting the critical role of metal ion-related defects in controlling the optical and electrochemical properties, towards potential solar absorption and energy storage applications.

25 ENERGY STORAGE↗

DOE OSTI2025

Elucidating the phase transformations and grain growth behavior of O3-type sodium-ion layered oxide cathode materials during high temperature synthesis

Understanding the formation mechanism of layered oxide cathodes via solid-state synthesis is imperative to achieving controllability over their materials properties and electrochemical behaviors. In this work, we investigate the phase and microstructure evolution during the synthesis of NaNi 1/3 Fe 1/3 Mn 1/3 O 2 , a model sodium-ion layered oxide cathode, using a combination of imaging, diffraction, and spectroscopic techniques. We unravel the synthetic mechanistic pathways involved in the high-temperature calcination reaction, as well as elaborate the synthesis-microstructure-performance relationship of this material. The formation of the final layered oxide phase involves a gradual transformation through a sodiated oxyhydroxide intermediate. During the reaction, the precursor dehydration reaction dominates at 250–550 °C, while the major sodiation reaction occurs at 550–850 °C. Alongside multiple stages of phase transformations, the final grain structure formation occurs through the continuous growth of the (003) and (104) facets. During the reaction, Mn acts as the charge-compensating element and exhibits depth-dependent characteristics. When the sodiation reaction dominates over dehydration, the reaction intermediates undergo gradual electronic structure changes with increasing temperature, as indicated by the spectral features of TM3d-O2p hybrid states. Calcination duration is also a critical parameter governing the microstructure, surface reactivity, phase fraction distribution and electrochemical performance of the material. The optimal calcination duration was determined to be 18 hours at 850 °C under the conditions evaluated here. Calcination beyond this duration was found to be detrimental to electrochemical performance due to Na and O loss and heterogeneous sodium distribution throughout the particles. Our work sheds light on the complex crystallographic-chemical-microstructural evolution of sodium ion layered oxide cathodes and provides insight into precisely tuning material properties which are intimately linked to battery performances.

25 ENERGY STORAGE↗

DOE OSTI2025

Extending the Mott–Gurney law to one-dimensional nonplanar diodes using point transformations

Recent studies have applied variational calculus, conformal mapping, and point transformations to generalize the one-dimensional (1D) space-charge limited current density (SCLCD) and electron emission mechanisms to nonplanar geometries; however, these assessments have focused on extending the Child–Langmuir law (CLL) for SCLCD in vacuum. Since the charge in the diode is independent of the coordinate system (i.e., covariant), we apply bijective point transformations to extend the Mott–Gurney law (MGL) for the SCLCD in a collisional or semiconductor gap to nonplanar 1D geometries. This yields a modified MGL that replaces the Cartesian gap distance with a canonical gap distance that may be written generally in terms of geometric scale factors that are known for multiple geometries. We tabulate results for common geometries. Such an approach may be applied to any current density, including non-space-charge limited gaps and SCLCD that may fall between the CLL and MGL.

Garner, Allen L. [Purdue Univ., West Lafayette, IN↗

DOE OSTI2026

Scalable quantum computational science: A perspective from block-encodings and polynomial transformations

Significant developments made in quantum hardware and error correction recently have been driving quantum computing toward practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented, including the construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this perspective serves as a gentle introduction to state-of-the-art quantum algorithms for the computational science community and inspires future development of scalable quantum computational science methodologies that bridge theory and practice.

Bayesian inference↗

DOE OSTI2024

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Neutrino Flavor Transformation in Neutron Star Mergers

Here, we present the first numerical relativity simulations including neutrino flavor transformations that could result from flavor instabilities, quantum many-body effects, or potential beyond standard model physics in neutron star mergers. We find that neutrino flavor transformations impact the composition and structure of the remnant, potentially leaving an imprint on the postmerger gravitational-wave signal. They also have a significant impact on the composition and nucleosynthesis yields of the ejecta.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Training toward significance with the decorrelated event classifier transformer neural network

Experimental particle physics uses machine learning for many tasks, where one application is to classify signal and background events. This classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. In this work, an event classifier transformer is proposed to bin an analysis region, in which the network is trained with special techniques. The techniques developed here can enhance the significance and reduce the correlation between the network’s output and the reconstructed mass. It is found that this trained network can perform better than boosted decision trees and feed-forward networks. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2025

Many-Body Quantum Catalysts for Transforming Between Phases of Matter

A catalyst is a substance that enables otherwise impossible transformations between states of a system, without being consumed in the process. In this work, we apply the notion of catalysts to many-body quantum physics. In particular, we construct catalysts that enable transformations between different symmetry-protected topological (SPT) phases of matter using symmetric finite-depth quantum circuits. We discover a wide variety of catalysts, including GHZ-like states that spontaneously break the symmetry, gapless states with critical correlations, topological orders with symmetry fractionalization, and spin-glass states. These catalysts are all united under a single framework that has close connections to the theory of quantum anomalies, and we use this connection to put strong constraints on possible pure- and mixed-state catalysts. We also show how the catalyst approach leads to new insights into the structure of certain phases of matter, and to new methods to efficiently prepare SPT phases with long-range interactions or projective measurements.

quantum many-body systems↗

DOE OSTI2026

A Unified Design Theory for Multi-Port Polyphase Transformers Enabling Scalable Power-Multiplexed EV Fleet Charging Systems

This paper presents a unified analytical design theory for multi-port polyphase transformers, targeting scalable and isolated high-power Electric Vehicle (EV) fleet charging systems with power multiplexing capability. As fleet electrification accelerates, conventional one-to-one charger architectures face significant challenges in infrastructure cost, peak power demand, and low utilization of installed power electronics. Power-multiplexed charging architectures, which dynamically distribute power from a shared pool of converter modules across multiple vehicles, have emerged as a promising solution. However, such architectures require scalable, isolated multi-port power interfaces capable of routing energy among multiple inputs and outputs, whose design remains complex and dependent on iterative modeling. To address this gap, the proposed theory provides closed-form expressions for self-inductance, leakage inductance, and mutual coupling terms for arbitrary multi-phase, multi-port transformer structures. The formulation enables direct synthesis of isolated multi-input and multi-output resonant converter systems without reliance on geometry-specific finite-element analysis or extensive parameter extraction. This capability is particularly critical for power-multiplexed systems, where modular converter structures must interface with multiple vehicles while maintaining galvanic isolation and flexible power allocation. The effectiveness of the proposed framework is demonstrated through the design of a 360 kW multi-phase system operating over a 700–900 VDC input and 400–1250 VDC output range. PLECS simulation results confirm accurate prediction of system behavior and validate the applicability of the approach to multi-port, power-multiplexed charging scenarios. The proposed method significantly reduces design complexity while enabling scalable, cost-effective, and fully utilized EV fleet charging infrastructure.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗

DOE OSTI2024

Transforming Science Through Software: Improving While Delivering 100×

The U.S. Department of Energy (DOE) Exascale Computing Project (ECP) funded the development of new (and the transformation of important existing) applications, libraries, and tools that realized improvement in performance and capabilities of often 100 times or more on emerging exascale computers. This exceptional gain inspired the title of this special issue: Transforming Science through Software: Improving while delivering 100X. The term 100X refers to advancing capabilities in modeling, simulation, and analysis by a factor of 100 or more using some combination of new algorithms, optimization techniques, software libraries, and programming models, coupled with the next generation of hardware for high-performance computing (HPC). The papers in this issue share experiences with the practice and science of scientific software development, with an emphasis on developing a coherent, portable, and sustainable HPC software ecosystem for next-generation computational science. Finally, we hope to foster expanded community efforts related to the fundamental role of sustainable scientific software ecosystems in advancing the computing sciences.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

SOHIP Abel Transform and Onion Peeling Model Module

This software provides tools for analyzing and modeling physical systems using mathematical transforms and layered models. It includes (1) functions for performing the Abel transform, which is used to relate measurements of bending angles to properties such as refractive index and radius in a medium. The code can compute bending angles from input profiles and also reconstruct these profiles from observed data; (2) the functions for modeling systems with multiple layers using an onion-peeling approach, allowing users to simulate and analyze the behavior of layered materials or structures. These capabilities are useful for researchers and engineers working in fields such as optics, atmospheric science, and materials analysis, enabling them to interpret and model data from experiments or simulations relates to refraction in spherical symmetric medium.

Xu, Shuang [Lawrence Livermore National Laboratory↗

DOE OSTI2024

Broadband rapid-scanning phase-modulated Fourier transform electronic spectroscopy

We present a phase-modulated approach for ultrabroadband Fourier transform electronic spectroscopy. To overcome the bandwidth limitations and spatial chirp introduced by acousto-optic modulators (AOMs), pulses from a 1 µm laser are modulated using AOMs prior to continuum generation. This phase modulation is transferred to the continuum generated in a yttrium aluminum garnet crystal. Separately generated phase-modulated continua in two arms of a Mach-Zehnder interferometer interfere with the difference of their modulation frequencies, enabling physical under-sampling of the signal and the suppression of low-frequency noise. By interferometrically tracking the relative time delay of the continua, we perform continuous, rapid-scanning Fourier transform electronic spectroscopy with a high signal-to-noise ratio and spectral resolution. As proof of principle, we measure the linear absorption and fluorescence excitation spectra of a laser dye and various biological samples.

Javed, Ariba↗