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DOE OSTI2026

PyJMAK: An Open-Source Python Toolkit for Modeling Solid-State Metallurgical Phase Transformations

Accurate prediction of metallurgical phase transformations is an essential basis for autonomous optimization and rapid part qualification. Several methods can be used to estimate the evolution of phase fractions such as JMAK kinetics-based models, phase-field models, thermodynamic models, and data-driven machine learning models. Thermodynamic and phase-field-based methodologies solve multiphysics equations requiring numerous calibration parameters and significant computational resources. As a result, the computation domain is limited to a point or on order of micron-meters. The data-driven models rely on large datasets from experiments and simulations. While the JMAK model only provides information about phase fraction evolution, it can predict this evolution in near real-time using thermal history and thermodynamic data without restriction on the domain. JMAK models have been popularly used by researchers to model phase transformations occuring during additive manufacturing or over arbitrary temperature profiles. Commercial proprietary software such as Abaqus and Ansys or closed-source in-house implementations offer the ability to model JMAK based kinetics to predict phase transformation. However, these software packages are not open-source or freely available for use and development in conjunction with manufacturing machines, sensors, and machine learning algorithms. In addition, the use of the model is restricted by a license token. In contrast, given temperature profiles at multiple points in the domain, this Python-based PyJMAK model can compute phase evolution in parallel due to its stand-alone modular, voxel-based structure, and it can be executed on high-performance computing resources without any license restrictions.

Prabhune, Bhagya [Oak Ridge National Laboratory (O↗

DOE OSTI2025

Numerical Simulation of Electron Magnetohydrodynamics with Landau-quantized Electrons in Magnetar Crusts

Abstract In magnetar crusts, magnetic fields are sufficiently strong to confine electrons into a small to moderate number of quantized Landau levels. This can have a dramatic effect on the crust's thermodynamic properties, generating field-dependent de Haas–van Alphen oscillations. We previously argued that the large-amplitude oscillations of the magnetic susceptibility could enhance the ohmic dissipation of the magnetic field by continuously generating small-scale, rapidly dissipating field features. This could be important to magnetar field evolution and contribute to their observed higher temperatures. To study this, we performed quasi-3D numerical simulations of electron MHD in a representative volume of neutron star crust matter, for the first time including the magnetization and magnetic susceptibility resulting from Landau quantization. We find that the potential enhancement in the ohmic dissipation rate due to this effect can be a factor ∼3 for temperatures of the order of 10 8 K, and ∼4.5 for temperatures of the order of 5 × 10 7 K, depending on the magnetic field configuration. The nonlinear Hall term is crucial to this amplification: without it, the magnetic field decay is only enhanced by a factor ≲2 even at 5 × 10 7 K. These effects generate a high wavenumber plateau in the magnetic energy spectrum associated with the small-scale de Haas–van Alphen oscillations. Our results suggest that this mechanism could help explain the magnetar heating problem, though due to the effect's temperature-dependence, full magneto-thermal evolution simulations in a realistic stellar model are needed to judge whether it is viable explanation.

Rau, Peter B. (ORCID:0000000152209277)↗

DOE OSTI2024

Towards a Circular Economy for PET Bottles in the U.S. - 4P Model

The United States generates the most plastic waste of any country. Along with that GHG emissions from the global plastic economy are expected to increase to 15% of the global carbon budget by 2050. It is imperative that plastic recycling is made a reality to reduce both plastic pollution in the environment and GHG emissions. A portfolio of end-of-life strategies must be implemented to minimize environmental impacts and retain valuable plastic material, but it is challenging to compare options that generate products with different utility and lifetime. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. The novel analysis framework, Plastic Parallel Pathways Platform (4P) has been equipped with consequential life cycle assessment, techno-economic analysis, and a plastic circularity indicator to estimate the greenhouse gas (GHG) emissions, circularity, and cost of polyethylene terephthalate (PET) down-cycling to lower-quality resin, closed-loop recycling to food-grade PET bottles, up-cycling to fiber-reinforced plastic (FRP), and conversion to non-plastic products (electricity, oil) on a United States economy-wide basis. Integrating system dynamics into this robust plastics model that already incorporates techno-economics, circularity, and environmental impacts will enable identification of key bottlenecks between manufacturers, waste sorters, and reclaimers that currently prevent rapid decarbonization of the plastics economy. System dynamics (SD) explore the evolution of activities and technologies based on changed macro parameters such as plastic demand and supply, market shifts, and cross-sectoral interactions. This project particularly aims to explore the interplay between, waste collection, plastic waste sorting, recycling, and manufacturing, as well as the effect of plastic bale quality and plastic reuse initiatives on the surrounding process stages. This functionality will facilitate combinatory analysis in which a portfolio of end-of-life pathways are assessed simultaneously, with the exact makeup of that portfolio affected by parameters such as technology scales, resource constraints, and waste mitigation efforts. Integrating SD with the 4P framework enables analyzing the effect of increased revenue and reinvestment into improving process efficiencies, sorting and collection quantities. Through that, market effects of increased recycled resin availability can be studied for the plastics systems model for the US. The results will help identify technical or economic bottlenecks that currently limit efforts to decarbonize the U.S. plastics economy.

carbon↗

DOE OSTI2024

Towards a Circular Economy for PET Bottles in the U.S. - 4P Framework

circular economy↗

DOE OSTI2024

Domesticating the green alga Scenedesmus obliquus

Microalgae cultivation processes exist only for a few species used in commercial applications. Their domestication is still in its infancy, with major advances required, specifically to overcome limiting factors in microalgae production. The goal is to develop superior strains without the need of genetic technologies that would require lengthy regulatory permits, and often limit consumer acceptance. Adaptive Laboratory Evolution techniques, alone or in conjunction with sexual recombination, can allow for rapid develop of improved strains and their industrial production. Approaches include increased photon utilization efficiency by cultures operating under full sunlight conditions due to higher light saturation levels, allowing for higher productivities under outdoor conditions. Decades of research yielded some promising results under controlled conditions with a few specific mutant strains. However, these failed to achieve the anticipated higher productivities in actual algal mass cultures, in part due to the inability of mutations to overcome photoinhibition, reactive oxygen species, and other pleiotropic impacts on photosynthesis.

09 BIOMASS FUELS↗

DOE OSTI2024

The R -process Alliance: Fifth Data Release from the Search for R -process-enhanced Metal-poor Stars in the Galactic Halo with the GTC

Understanding the abundance pattern of metal-poor stars and the production of heavy elements through various nucleosynthesis processes offers crucial insights into the chemical evolution of the Milky Way, revealing primary sites and major sources of rapid neutron-capture process (r-process) material in the Universe. In this fifth data release from the R-Process Alliance (RPA), we present the detailed chemical abundances of 41 faint (down to V = 15.8) and extremely metal-poor (down to [Fe/H] = -3.3) halo stars selected from the RPA. We obtained high-resolution spectra for these objects with the HORuS spectrograph on the Gran Telescopio Canarias. We measure the abundances of light, α, Fe-peak, and neutron-capture elements. We report the discovery of five carbon-enhanced metal-poor, one limited-r, three r-I, and four r-II stars, and six Mg-poor stars. We also identify one star of a possible globular cluster origin at an extremely low metallicity at [Fe/H] = -3.0. This adds to the growing evidence of a lower-limit metallicity floor for globular cluster abundances. We use the abundances of Fe-peak elements and the α-elements to investigate the contributions from different nucleosynthesis channels in the progenitor supernovae. We find the distribution of [Mg/Eu] as a function of [Fe/H] to have different enrichment levels, indicating different possible pathways and sites of their production. We also reveal differences in the trends of the neutron-capture element abundances of Sr, Ba, and Eu of various r-I and r-II stars from the RPA data releases, which provide constraints on their nucleosynthesis sites and subsequent evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Impact of QCD energy evolution on observables in heavy-ion collisions

Here, we study how the inclusion of energy dependence as dictated by quantum chromodynamic (QCD) small-x evolution equations affects key observables in ultra-relativistic heavy-ion collisions. Specifically, we incorporate JIMWLK evolution into the IP-Glasma framework, which serves as the initial condition for a simulation pipeline that includes viscous relativistic hydrodynamics and a hadronic afterburner. This approach enables a consistent modeling of highly energetic nuclei across varying Bjorken-x values, which are relevant for different collision energies and rapidity regions. In comparison to the standard IP-Glasma setup without small-x evolution, we observe pronounced changes in particle multiplicities and spectral distributions, especially in smaller systems and at the highest available energies. We further explore effects on anisotropic flow observables and correlations between mean transverse momentum and elliptic flow. Our findings underscore the critical role of nonlinear QCD evolution in accurately modeling the early stages of heavy-ion collisions, as well as its implications for extracting transport properties of the quark-gluon plasma.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

An Unconventional Dark Radical Chemistry in Dense Molecular Clouds: Directed Gas-Phase Formation of Naphthyl Radicals

The synthetic pathways to aromatic molecules inside photon-shielded dense molecular clouds remain a fundamental, unsolved enigma in astrochemistry and astrophysics, with low-temperature molecular growth routes involving aromatic radicals, such as prototype bicyclic naphthyl (C 10 H 7 • ), implicated as key sources. Here, exploiting crossed molecular beam experiments augmented by electronic structure calculations, unexpected pathways are exposed leading to the gas-phase formation of 1- and 2-naphthyl via barrierless bimolecular reactions of atomic carbon (C) with indene (C 9 H 8 ) and of dicarbon (C 2 ) with styrene (C 8 H 8 ) accompanied by ring expansion and cyclization together with aromatization. These facile routes challenge conventional wisdom that aromatic radicals are formed in deep space solely via “bright” gas-phase photochemistry of their closed-shell polycyclic aromatic hydrocarbon (PAH) precursors. A hitherto disregarded “dark” aromatic radical chemistry with aromatic radicals synthesized via gas-phase reactions offers new concepts on the chemical evolution of the chemistry of dark molecular clouds eventually culminating in the rapid formation of aromatics, fullerenes, and carbonaceous nanostructures.

Aromatic compounds↗

DOE OSTI2025

Accelerating Discovery of Solid‐State Thin‐Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment

Thin‐film solid‐state metal dealloying (thin‐film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high‐throughput characterization of thermal treatment parameters is demonstrated using a laser‐based thermal treatment to create temperature gradients on single thin‐film samples of Nb‐Al/Sc and Nb‐Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X‐ray multimodal and high‐throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified. While the oxidation may influence nanostructure formation during thin‐film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. Further, this approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin‐film SSMD systems with targeted nanostructures.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations

This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Anticipating Technical Expertise and Capability Evolution in Research Communities Using Dynamic Graph Transformers

The ability to anticipate global technical expertise and capability evolution trends is essential for national and global security, especially in safety-critical domains such as nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). Here, in this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. We implement a dynamic graph transformer (DGT) neural architecture, which pushes the state-of-the-art graph neural network models by: 1) forecasting heterogeneous (rather than homogeneous) nodes and edges; and 2) relying on both discrete- and continuous-time inputs. We demonstrate that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30%–80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance when previously unseen nodes appear in the test data for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice versa in the AI domain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Tomography of pions and kaons in the QCD vacuum: Transverse momentum dependent parton distribution functions

We evaluate the pion and kaon transverse momentum dependent parton distribution functions in the instanton liquid model, a model of the QCD vacuum at low resolution. The relevant transverse momentum distributions (TMDs) are factored into a constituent quark distribution times a rapidity dependent soft factor from staple-shaped Wilson lines, for fixed parton longitudinal momentum and transverse separation. The results are evolved to higher rapidities using the Collins-Soper kernel and higher resolution using renormalization group evolution. The comparison to existing extractions of pion TMDs from Drell-Yan data is briefly discussed.

classical solutions in field theory↗

DOE OSTI2024

An approach to combine neutron and ion irradiation data to accelerate material qualification for nuclear reactors

Next-generation nuclear power plants are generally characterized by higher operating temperatures, increased neutron fluences and energies, and distinct corrosive coolant environments versus the existing light water reactor fleet. Whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. Because microstructure has a large impact on bulk material properties, limited neutron irradiation data at lower damage levels can in principle be combined with accelerated ion testing results and modeling and simulation to form an accurate prediction of microstructure evolution and select properties under different neutron irradiation conditions and at higher damage levels. The objective of this work is to present a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame. A brief review of the regulatory landscape for materials in nuclear environments is presented, followed by additional overviews to understand the current state of the art for correlation of materials properties across radiation environments using experimental and computational methodologies. Finally, the roles of academia, national laboratories, and industry in the advancement of this accelerated materials qualification framework are discussed as a path forward, with possible case studies presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Sustainable Synthesis of NiMo Alloy Nanoparticles for Hydrogen Evolution Catalysis Using a Recyclable Ionic Liquid Solvent

Advancing the sustainability of nanoparticle electrocatalyst synthesis requires reducing solvent waste without compromising catalytic performance. Here, we report a rapid, microwave‐assisted colloidal synthesis of NiMo alloy nanoparticles in the ionic liquid 1‐butyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide (BMIM‐NTf 2 ), which serves as both reaction medium and recyclable solvent. NiMo nanoparticles with tunable compositions were obtained within 1 min at 230 °C, with Ni 0.86 Mo 0.14 giving the highest activity toward the hydrogen evolution reaction (HER), achieving an onset potential of −129 mV, an overpotential at 10 mA cm −2 of −290 mV versus RHE in acidic electrolyte, and >99% Faradaic efficiency. The ionic liquid was recovered and purified via aqueous NH 4 OH extraction, removing over 99% of residual Mo species, and reused for up to five successive syntheses. Nanoparticles prepared in recycled BMIM‐NTf 2 retained comparable crystallinity, morphology, and catalytic activity, with stable HER performance over 45 h of electrolysis. These results demonstrate that ionic liquid recycling can improve the sustainability of the preparation of NiMo nanoparticle electrocatalysts while preserving performance and material integrity.

bimetallic alloy↗

DOE OSTI2024

Smoothing and flattening the universe through slow contraction versus inflation

In a systematic study, we use an equivalent pair of improved numerical relativity codes based on a tetrad-formulation of the classical Einstein-scalar field equations to examine whether slow contraction or inflation (or both) can resolve the homogeneity, isotropy and flatness problems. Our finding, based on a set of gauge/frame invariant diagnostics and the models considered, is that slow contraction robustly and rapidly smooths and flattens spacetime beginning from initial conditions that are outside the perturbative regime of the flat Friedmann-Robertson-Walker metric, whereas inflation fails these tests. We present new numerical evidence supporting the conjecture that the combination of ultralocal evolution and an effective equation-of-state with pressure much greater than energy density is the key to having robust and rapid smoothing. The opposite of ultralocality occurs in expanding spacetimes, which is the leading obstruction to smoothing following a big bang.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

METHOD FOR EXPOSING IRRADIATED FUEL CLADDING TO RAPID THERMAL TRANSIENTS

Reactor core materials exposed to rapid thermal transients during accidents can experience significant changes in mechanical properties as irradiation hardness is recovered. Understanding material performance during transients using computer modeling requires accurate descriptions of mechanical property evolution with time and temperature. A simple experimental capability was developed to expose irradiated Zircaloy to a simulated thermal transient using immersion in molten tin (at 675°C) to rapidly heat and hold before rapidly cooling with a water quench. Multiple heating-cooling cycles using a nonirradiated tensile specimen showed simplicity and viability. Based on preliminary test results, a robust system was established for routine evaluation of simulated thermal accident scenarios in the Low Activation Materials Design and Analysis (LAMDA) laboratory at Oak Ridge National Laboratory (ORNL). This paper summarizes system development and nonirradiated Zircaloy commissioning test results. Future experiments will conduct tensile tests on irradiated Zircaloy after various rapid temperature profiles.

Byun, TS↗

DOE OSTI2024

The role of manganese in CoMnO x catalysts for selective long-chain hydrocarbon production via Fischer-Tropsch synthesis

Cobalt is an efficient catalyst for Fischer–Tropsch synthesis (FTS) of hydrocarbons from syngas (CO + H 2 ) with enhanced selectivity for long-chain hydrocarbons when promoted by Manganese. However, the molecular scale origin of the enhancement remains unclear. Here we present an experimental and theoretical study using model catalysts consisting of crystalline CoMnO x nanoparticles and thin films, where Co and Mn are mixed at the sub-nm scale. Employing TEM and in-situ X-ray spectroscopies (XRD, APXPS, and XAS), we determine the catalyst’s atomic structure, chemical state, reactive species, and their evolution under FTS conditions. We show the concentration of CH x , the key intermediates, increases rapidly on CoMnO x , while no increase occurs without Mn. DFT simulations reveal that basic O sites in CoMnO x bind hydrogen atoms resulting from H 2 dissociation on Co 0 sites, making them less available to react with CH x intermediates, thus hindering chain termination reactions, which promotes the formation of long-chain hydrocarbons.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Double diffraction imaging of x-ray induced structural dynamics in single free nanoparticles

Abstract Because of their high photon flux, x-ray free-electron lasers (FEL) allow to resolve the structure of individual nanoparticles via coherent diffractive imaging (CDI) within a single x-ray pulse. Since the inevitable rapid destruction of the sample limits the achievable resolution, a thorough understanding of the spatiotemporal evolution of matter on the nanoscale following the irradiation is crucial. We present a technique to track x-ray induced structural changes in time and space by recording two consecutive diffraction patterns of the same single, free-flying nanoparticle, acquired separately on two large-area detectors opposite to each other, thus examining both the initial and evolved particle structure. We demonstrate the method at the extreme ultraviolet (XUV) and soft x-ray Free-electron LASer in Hamburg (FLASH), investigating xenon clusters as model systems. By splitting a single XUV pulse, two diffraction patterns from the same particle can be obtained. For focus intensities of about 2 × 10 12 W cm −2 we observe still largely intact clusters even at the longest delays of up to 650 picoseconds of the second pulse, indicating that in the highly absorbing systems the damage remains confined to one side of the cluster. Instead, in case of five times higher flux, the diffraction patterns show clear signatures of disintegration, namely increased diameters and density fluctuations in the fragmenting clusters. Future improvements to the accessible range of dynamics and time resolution of the approach are discussed.

Sauppe, M. (ORCID:0000000163123102)↗