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DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron

Efforts in the study of the longitudinal behavior of charged particles in the Fermilab Booster can be catalyzed with an image of the two-dimensional phase space distribution. In the past, tomography has been extensively employed in the reconstruction of the phase space in accelerators such as the Recycler at Fermilab and the Proton Synchrotron Booster at CERN. However, such a capability had yet to realize for the Fermilab Booster synchrotron. In this work, the first successful tomographic phase space reconstruction of a low-energy Booster bunch is presented along with validation metrics. A numerical turn-by-turn model of the longitudinal particle dynamics in the Booster has been implemented, which utilizes a fast, map-based particle transport algorithm. Using a sinogram generated from the Wall Current Monitor, the iterative reconstruction algorithm recovers a discretized image of the original phase space distribution at variable resolution. The reconstruction result shows low root-mean-square error and a rapid convergence toward the solution, providing strong evidence of accuracy. Future and ongoing work includes modeling high-energy bunches above transition and using tomography to infer certain machine parameters such as synchronous phase, peak gap voltage, and synchronous energy in addition to the phase space distribution.

Ebeid, Safi [Unlisted]↗

DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron (Abstract Only)

Ebeid, Safi [Unlisted, US]↗

DOE OSTI2026

Longitudinal Phase Space Tomography for the Booster Synchrotron

Efforts in the study of the longitudinal behavior of charged particles in the Fermilab Booster can be catalyzed with an image of the two-dimensional phase space distribution. In the past, tomography had been employed in the reconstruction of the phase space in accelerators such as the Recycler at Fermilab and the Proton Synchrotron Booster at CERN. However, such a capability had yet to realize for the Fermilab Booster synchrotron. In this work, the first successful tomographic phase space reconstruction of a low-energy Booster bunch is presented along with validation metrics. A numerical turn-by-turn model of the longitudinal particle dynamics in the Booster has been implemented, which utilizes a fast, map-based particle transport algorithm. Using a sinogram generated from the Wall Current Monitor signal, the iterative reconstruction algorithm recovers a discretized image of the original phase space distribution at variable resolution. The reconstruction result shows low root-mean-square error and a rapid convergence toward the solution, providing strong evidence of accuracy. Future and ongoing work includes modeling high-energy bunches above transition and using tomography to infer certain machine parameters such as synchronous phase, peak gap voltage, and synchronous energy in addition to the phase space distribution.

Ebeid, Safi [Unlisted; Fermilab]↗

DOE OSTI2024

Design Basis Model for Hosting Small Modular Reactors

An aggressive transition from fossil fuels to other types of energy implies the need to construct a large number of nuclear power plants in the near future. However, the real and perceived risks of nuclear energy remain a significant impediment to this transition. This paper describes a comprehensive work process that combines the rigor of model-based systems engineering (MBSE) with 1) the Idaho National Laboratory's (INL) decades of experience with small reactors and with 2) modern project delivery processes. The objective is to reduce the risk of building new facilities or converting existing facilities to nuclear power generation.

42 ENGINEERING↗

DOE OSTI2024

Foundation Models for the Electric Power Grid

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

AI-based power flow simulation↗

DOE OSTI2024

S U ( 2 ) principal chiral model with tensor renormalization group on a cubic lattice

We study the continuous phase transition and thermodynamic observables in the three-dimensional Euclidean S U ( 2 ) principal chiral field model with the triad tensor renormalization group and the anisotropic tensor renormalization group methods. Using these methods, we find results that are consistent with previous Monte Carlo estimates and the predicted renormalization group scaling of the magnetization close to criticality. These results bring us one step closer to studying finite-density quantum chromodynamics in four dimensions using tensor network methods. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Coupling strongly correlated electron systems to a tunable electronic reservoir

Here, we study the effect of coupling an electronic reservoir to a Hubbard model and to a dimer Hubbard model. This is motivated by recent experiments on the effect of illumination on the insulator-metal transition in a vanadium oxides and photoconductive cadmium sulfide heterostructure. We model the system as an electronic reservoir hybridized to the correlated system. We assume that the light intensity controls the hybridization coupling strength. We find that the light intensity acts similarly as the temperature in the weak interaction regime. This is consistent with the role played by electronic reservoirs in out-of-equilibrium systems. In contrast, qualitative differences appear at strong coupling. We show that modeling the V 2 ⁢O 3 compound with a Hubbard model, our results describe qualitatively well the observed illumination-driven suppression of the insulator-metal transition. In contrast, in the DHM results fail to capture the mild suppression observed in the case of VO 2 . This indicates that the lattice may play an important role in this case.

Materials Science↗

DOE OSTI2025

The glass-ceiling convective regime and the origin and diversity of coronae on Venus

Venus and Earth are rocky planets of roughly the same size and bulk density, yet their surface volcanic and tectonic features appear substantially different. On Venus, the coexistence of large volcanic highlands—interpreted as the surface expression of long-lived mantle plumes—alongside coronae, smaller features thought to be caused by transient thermal diapirs, remains enigmatic. Using two-dimensional numerical models of mantle convection with sharp and broad mineral phase transitions for pyrolite, we show that both scales of upwellings can be generated in a stagnant lid planet with an interior temperature 250 to 400 K warmer than Earth’s. The smaller plumes originate from a ~600 km deep internal layer that exists as a consequence of the different sequence of mineral phase transitions that occur in warmer mantles less processed and differentiated by partial melting and volcanism. Future models that include melting will provide further tests of our hypothesis.

Science & Technology - Other Topics↗

DOE OSTI2026

Organic Rankine Cycle Integration and Optimization for High Efficiency CHP Genset Systems (Final Technical Report)

This project successfully advanced the integration of Organic Rankine Cycle (ORC) technology with reciprocating engine–based combined heat and power (CHP) systems to improve electrical efficiency, total CHP efficiency, and grid-responsive operation. Over three budget periods, the work progressed from high-temperature ORC component development and thermodynamic model validation to next-generation system design, working fluid transition, and techno-economic analysis. Key technical accomplishments include development and validation of a thermodynamic model capable of accurately predicting ORC performance across an expanded temperature and pressure envelope; successful identification and validation of low-global-warming-potential (GWP) working fluids—most notably R1233zd(E)—as viable replacements for R245fa; and demonstration of scalable ORC architectures suitable for integration with 1–20 MW class reciprocating engines. These advances enable flexible CHP configurations that can increase electrical output while maintaining high overall utilization of available thermal energy. The project also produced a clean-sheet design for a next-generation ORC system targeting substantially higher power output per unit, supported by detailed component selection, heat exchanger evaluation, and system-level modeling. Techno-economic analyses indicate that ORC-enabled flexible CHP systems can meet or exceed Department of Energy (DOE) efficiency targets while providing value to both facility operators and the electric grid.. Late-stage testing of the largest next-generation ORC prototype identified limitations related to pump net positive suction head (NPSH) requirements and condenser flooding under certain operating conditions. Although these issues constrained full validation of that configuration within the project timeframe, they provided clear and actionable design guidance for future system refinements. Importantly, validated modeling, smaller-scale testing, and working fluid evaluations confirmed the technical viability of the overall approach. In aggregate, this project met its core objectives by establishing validated design tools, de-risking key ORC technologies for CHP applications, and defining a credible pathway toward commercialization of flexible, high-efficiency CHP systems. The results form a strong foundation for continued development and deployment beyond the conclusion of the DOE-funded effort.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

BMINN: Learning chemical potentials and parameters from voltage data for multi-phase battery modeling

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

25 ENERGY STORAGE↗

DOE OSTI2025

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design

Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Han, Ming [Univ. of Chicago, IL (United States)]↗

DOE OSTI2024

FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings (FTL) v1.0

Fusion Transfer Learning (FTL) model provides a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. The knowledge transfer process leverages a pre-trained neural encoder-decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL's capacity to capture transitional behaviors and dynamical features in plasma dynamics -- a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics (MHD) modes.

Bai, Zhe↗

DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗

DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗

DOE OSTI2026

Mechanics of pore array collapse and interaction in shock-compressed polymethyl methacrylate (PMMA)

Recent studies on dynamic pore collapse have revealed significant development of shear localization, which can lead to material failure in porous structures and hot spot generation in energetic materials. These findings have dramatically improved the understanding of failure mechanisms during pore collapse but also prompt further investigation of realistic porous materials. In particular, porous media consist of many pores and porous networks. Even in low-porosity materials, pores can form in close proximity during the manufacturing process, leading to the critical question of pore–pore interaction during collapse under dynamic loading conditions. This study investigates, via plate impact experiments coupled with high-speed internal digital image correlation and shadowgraphy techniques, the collapse of two pores in shock-compressed PMMA at stresses between 0.4 and 1 GPa. The results of these experiments provide new insights into shear localization in pore collapse, in addition to distinct interactions between pores. Shadowgraphy measurements reveal novel, direct visualization of shear band development and crack evolution from pore surfaces. Spacing between adiabatic shear bands is measured over a range of impact stresses and is predicted accurately by the Grady–Kipp model. Pore interactions are found to effect a transition in the impact stress threshold at which different failure mechanisms initiate and are also found to possibly influence preferential sites for shear cracking. Throughout the study, numerical and theoretical models are leveraged to understand shear localization behavior. The role of baroclinicity and wave interactions between the pores is used to elucidate interaction mechanisms between pores.

Lawlor, Barry P. [California Institute of Technolo↗

DOE OSTI2025

Exploring the role of 𝑑* hexaquarks on quark deconfinement and hybrid stars

We investigate the impact of the d*( 2380) hexaquark on the equation of state (EOS) of dense matter within hybrid stars (HSs) using the chiral mean-field model (CMF). The hexaquark is included as a new degree of freedom in the hadronic phase, and its influence on the deconfinement transition to quark matter is explored. We reparametrize the CMF model to ensure compatibility with recent astrophysical constraints, including the observation of massive pulsars and gravitational wave events. Our results show that the presence of d* significantly modifies the EOS, leading to a softening at high densities and a consequent reduction in the predicted maximum stellar masses. Furthermore, we examine the possibility of a first-order deconfinement phase transition within the context of the extended stability branch of slow stable HSs (SSHSs). We find that the presence of hexaquarks can delay the deconfinement phase transition and reduce the associated energy density gap, affecting the structure and stability of HSs. Our results suggest that, as the hexaquark appearance tends to destabilize stellar configurations, fine-tuning of model parameters is required to obtain both the presence of hexaquarks and quark deconfinement in these systems. In this scenario, the SSHS branch plays a crucial role in obtaining HSs with hexaquarks that satisfy current astrophysical constraints. Our work provides new insights into the role of exotic particles like d* in dense matter and the complex interplay between hadronic and quark degrees of freedom inside compact stellar objects.

Nuclear astrophysics↗

DOE OSTI2025

Impact of the nuclear equation of state on the formation of twin stars

Twin stars-two stable neutron stars (NSs) with the same mass but different radii have long been proposed to appear as a consequence of a possible first-order phase transition in NS matter. Within a meta-model for the EOS of hybrid stars, we revisit the viability of twin stars and its dependence on numerous parameters characterizing the EOS of nuclear matter, quark matter, and the phase transition between them. While essentially no experimental constraint exists for the last two, parameters characterizing the EOS of neutron-rich nucleonic matter have been constrained within various ranges by terrestrial experiments and astrophysical observations. Within these ranges, the impact of nuclear EOS and crust-core transition density on the formation of twin stars is studied. Here, it is found that the symmetry energy of neutron-rich nucleonic matter notably influences the formation of twin stars, particularly through its slope L and curvature K sym . Conversely, varying the EOS of symmetric nuclear matter within their currently known uncertainty ranges shows minimal influence on the formation of twin stars.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Tensor renormalization group approach to the $O(2)$ models via symmetry-twisted partition functions

We investigate critical phenomena in the $O(2)$ models using symmetry-twisted partition functions that can be efficiently computed within the tensor renormalization group framework. We first demonstrate, taking the three-dimensional model as an example, that symmetry-twisted partition functions detect the spontaneous breaking of global continuous symmetry. We then consider the same model in two dimensions, where the Berezinskii--Kosterlitz--Thouless (BKT) transition occurs. Since symmetry-twisted partition functions directly provide the helicity modulus at a finite twist angle, we determine the BKT transition point. These results are presented based on Ref.~\cite{Akiyama:2026dzg}. Finally, in addition to the original paper~\cite{Akiyama:2026dzg}, we apply this approach to the two-dimensional generalized $O(2)$ model and confirm that it successfully identifies the phase transitions between the ferromagnetic and nematic phases, as well as between the nematic and paramagnetic phases.

Akiyama, Shinichiro [Tsukuba U., CCS; Tokyo U., IC↗