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DOE OSTI2026

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Continuous Directed Evolution of a Short-Lived Plant Histidinol Dehydrogenase

Enzyme protein turnover accounts for about half the maintenance energy budget in plants. Slowing turnover i.e., extending the effective working life (Catalytic Cycles till Replacement, CCR) of short-lived enzymes is thus a rational strategy to conserve energy and carbon and raise crop productivity. Arabidopsis histidinol dehydrogenase (HDH) is a short-lived enzyme that can sustain lifeshortening damage from its aminoaldehyde reaction intermediate. We used the yeast OrthoRep continuous directed evolution system in a his4 Δ strain to raise cumulative HDH function and, by proxy, lifespan as functional enzymes, by selecting for growth rate while tapering histidinol concentration and escalating that of the inhibitor histamine. Improved HDH variants carried diverse nonsynonymous mutations and ranged 20-fold in level. Improved HDH performance was associated with higher HDH abundance in some cases and with greater catalytic efficiency or histamine resistance in others. These findings indicate that OrthoRep-based directed evolution can extend enzyme working life in vivo in addition to, as expected, altering kinetic properties.

directed evolution↗

DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗

DOE OSTI2020

Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

In this paper, we consider the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outer products of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in machine learning. The dth-order empirical moment tensor of a set of p observations of n variables is a symmetric d-way tensor. Our goal is to nd a low-rank tensor approximation comprising r $\ll$ p symmetric outer products. The challenge is that forming the empirical moment tensor costs O(pn d ) operations and O(n d ) storage, which may be prohibitively expensive; additionally, the algorithm to compute the low-rank approximation costs O(n d ) per iteration. Our contribution is avoiding formation of the moment tensor, computing the low-rank tensor approximation of the moment tensor implicitly using O(pnr) operations per iteration and no extra memory. This advance opens the door to more applications of higher-order moments since they can now be efficiently computed. We present numerical evidence of the computational savings and show an example of estimating the means for higher-order moments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

High Flux Isotope Reactor Low-Enriched Uranium Low Density Silicide Fuel Design Parameters

High Flux Isotope Reactor (HFIR) highly enriched uranium (HEU) to low-enriched uranium (LEU) conversion activities are ongoing as part of the Department of Energy (DOE) National Nuclear Security Administration (NNSA)’s nuclear nonproliferation mission. Design activities studying the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a low density uranium-silicide dispersion (U 3 Si 2 -Al) fuel, which has a uranium density of 4.8 gU/cm 3 . The goal of these studies is to generate several HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. The documented designs will serve as references for fuel fabrication and qualification activities. Recent advancements in modeling and simulation tools enable quick prototyping of fuel designs. Shift, a Monte Carlo neutron transport and depletion tool optimized for high-performance computing (HPC) architectures, is used for efficient fuel cycle and performance metrics calculations. The HFIR Steady State Heat Transfer Code (HSSHTC) is used to vet the thermal safety margin. Also, a new automation tool that connects all fuel design analysis steps, named Python HFIR Analysis and Measurement Engine (PHAME), has been developed to expedite the design study in an efficient and reproducible manner. Leveraging these tools, several candidate fuel designs were selected for varying fabrication complexity. This report provides design feature details for four selected HFIR LEU low density U 3 Si 2 -Al fuel designs and their corresponding performance and safety metrics. Nominal, best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities are provided for candidate fuel designs relevant to framing irradiation experiments to support fuel qualification efforts. Simulations show that the low density U 3 Si 2 -Al, with design features to enhance safety, can meet HEU core performance metrics and safety requirements if the reactor power is increased from 85 MW (HEU) to 95 MW (LEU) and if the active fuel length is increased from 50.80 cm (HEU) to 55.88 cm (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Comparative Life Cycle Analysis of Scalable Single-Junction and Tandem Perovskite Solar Cell (PSC) Systems

Efficient, low-cost solar cells based upon perovskites have the potential to transform the US and global energy portfolio and improve energy security if they can be manufactured in an environmentally sustainable manner. However, previous life cycle analyses (LCA) of perovskite solar cells (PSC) used lab recipes to project the environmental impact of industrial PSC production – using materials and processes that may not be representative of industrial productions due to high material waste and complex synthesis – and such studies were limited to a few single-junction technologies. Thus, the goal of this project was threefold: a) to review the most promising for industrial production single-junction and tandem PSC technologies, b) to build life-cycle materials inventories that reflect scalable production of these technologies, and c) to conduct comprehensive life-cycle analysis of these technologies and compare their environmental impact with those of established commercial technologies. In response, this project identified challenges for fabrication transitioning from laboratory to sustainable industrial production, developed life-cycle inventory (LCI) data for scaling to industrial production four promising single-junction and three tandem PSC systems and produced life-cycle-investigations using, as metrics, a complete spectrum of energy, environmental and ecological impact indicators. Special focus was given to the use of lead, silver and indium in PSC. The project used the Life Cycle Assessment (LCA) methodology as standardized by the Society of Environmental Toxicology and Chemistry (SETAC), ISO standards 14040 and 14044, and the International Energy Agency PVPS Task 12 LCA guidelines. LCA allows the calculation of a number of energy and environmental and impact categories, including energy cumulative energy demand (CED), global warming potential (GWP), human toxicity potential (HTP), eco-toxicity potential (ETP), abiotic resource depletion potential (ADP), acidification potential (AP), ozone depletion potential (ODP), photochemical oxidation potential (POP), eutrophication potential (EP). In addition to those, we calculated the Energy Pay-Back Time (EPBT) and the Energy Return On Investment (EROI). We provide LCA impacts for complete perovskite PV systems – including balance of system components – installed at three irradiation levels and considering reference and future potential module efficiencies. Finally, a sensitivity analysis on perovskite lifetime has been performed, considering 10, 20 and 30 years. Contribution analysis of the impacts of each material and layer of PSC shows, that the use of Pb in metalorganic PSC does not result in significant environmental impacts as the major contributions to environmental indicators arise from the use of Ag and encapsulation materials. Solution-based PSC manufacturing was found to be less impactful to the environment than vapor-based fabrication, and roll-to-roll (RtR) printing uses less energy and generates the lowest emissions. PSC produced with RtR manufacturing could reach the same Energy Return on Energy Investment (EROI) as that of crystalline-Si PV within 12 years of life, whereas the most energy demanding spray coating on rigid substrates, would require a 20-yr life to match the EROI of 30-yr lasting silicon PV. This work lays the foundation for sustainability investigations in a comparative context of large-scale production and deployment of PSC. The results of this project have the potential to have a significant impact on the future of PV manufacturing, by providing industry, policy-makers, and academia with insights necessary to choose which, if any, lead-based solar cell life cycles are environmentally sustainable.

14 SOLAR ENERGY↗

DOE OSTI2023

A Real-Time Degradation Estimation Approach for Batteries in PV and Battery Hybrid Plant Operation

Conventional cyclic degradation assessment for batteries is often limited in its ability to provide real-time degradation information during operation, because the estimation of degradation is commonly performed after a specific evaluation period. Modified methods can provide real-time evaluation but at the cost of high computation complexity. This paper proposes a novel estimation method that addresses these limitations. It efficiently determines the incremental degradation of any two consecutive state-of-charge (SOC) samples during battery operation, thus enabling real-time estimation. To validate the proposed method, measurement data from lithium-ion phosphate (LFP) battery testing was adopted, and the results were compared with the conventional model and the measured capacity. The results demonstrate accuracy over the evaluation period in terms of cumulative degradation, and a reduced computation cost compared with other methods. This approach has benefits in various applications, especially in cases where batteries experience irregular charging and discharging stress cycles such as PV and battery hybrid plants.

Wang, Shumeng↗

DOE OSTI2022

Stochastic evaluation of four-component relativistic second-order many-body perturbation energies: A potentially quadratic-scaling correlation method

A second-order many-body perturbation correction to the relativistic Dirac-Hartree-Fock energy is evaluated stochastically by integrating 13-dimensional products of four-component spinors and Coulomb potentials. The integration in the real space of electron coordinates is carried out by the Monte Carlo (MC) method with the Metropolis sampling, whereas the MC integration in the imaginary-time domain is performed by the inverse-CDF (cumulative distribution function) method. The computational cost to reach a given relative statistical error for spatially compact but heavy molecules is observed to be no worse than cubic and possibly quadratic with the number of electrons or basis functions. This is a vast improvement over the quintic scaling of the conventional, deterministic second-order many-body perturbation method. The algorithm is also easily and efficiently parallelized with demonstrated 92% strong scalability going from 64 to 4096 processors for a fixed job size.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Syracuse University Industrial Assessment Center (Final Report)

Syracuse University Industrial Assessment Center (SU-IAC) project is focused on the two primary objectives: (i) educate undergraduate and graduate students in engineering and associated disciplines on the concepts of energy sustainability and to provide hands-on training by performing energy assessment at industrial facilities to become future energy efficiency experts, and (ii) offer energy assessments to small and mid-sized manufacturing companies in SU-IAC 2 the greater New York State region, perform analytical engineering work in support of assessment recommendations, and to report the results of those analyses to the client companies, to DOE, and to Field office personnel Through this project period, SU-IAC successfully trained 77 engineering students who were undergraduate or graduate students at Syracuse University College of Engineering, or at SU- IAC’s satellite center at Clarkson University. Each of these students received comprehensive ‘in-class’ technical training, safety training, and hands-on field training. The students performed various tasks related to energy assessment at the manufacturing facilities, including billing analysis, energy use reduction recommendations, and report preparation. Through this project period, a total of 34 students have completed all requirements of the SU-IAC program and have received the US DoE Certificate on Energy Efficiency, with the remaining students continuing at various stages of field work or training. During this project period, due to Covid-related measures in New York State, Syracuse University campus and most of the manufacturing facilities were completely closed or were under severe restrictions for access, for an extended period from March 2020 through April 2021. The closures affected our project execution, with the project Sponsor offering structured relief to carry-out a modified project during the affected budget periods. During this project period, SU-IAC student teams, led by its Director, completed a total of 77 energy assessments at small- and medium-scale manufacturing facilities located in New York State. For these facilities, SU-IAC prepared and presented details for 569 assessment recommendations (ARs). The facilities implemented a total of 258 of these ARs, realizing annual average energy savings in the range of $\$ $3,300 to $\$ $55,000 per facility. Cumulatively, these facilities have reduced their “first year after implementation” energy use by a total of 4.37 million kwh, and fuel use by a total of 27.7 billion BTU. SU-IAC project has successfully met the two primary objectives (and the associated sub objectives) and helped the manufacturing facilities in New York State achieve quantitative energy use reductions as a result of this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Cosmic-ray anisotropies in right ascension measured by the Pierre Auger Observatory

We present measurements of the large-scale cosmic-ray anisotropies in right ascension, using data collected by the surface detector array of the Pierre Auger Observatory over more than 14 years. We determine the equatorial dipole component, $\vec{d}_\perp$, through a Fourier analysis in right ascension that includes weights for each event so as to account for the main detector-induced systematic effects. For the energies at which the trigger efficiency of the array is small, the ``East-West'' method is employed. Besides using the data from the array with detectors separated by 1500 m, we also include data from the smaller but denser sub-array of detectors with 750 m separation, which allows us to extend the analysis down to $\sim 0.03$ EeV. The most significant equatorial dipole amplitude obtained is that in the cumulative bin above 8~EeV, $d_\perp=6.0^{+1.0}_{-0.9}$%, which is inconsistent with isotropy at the 6$\sigma$ level. In the bins below 8 EeV, we obtain 99% CL upper-bounds on $d_\perp$ at the level of 1 to 3 percent. At energies below 1 EeV, even though the amplitudes are not significant, the phases determined in most of the bins are not far from the right ascension of the Galactic center, at $\alpha_{\rm GC}=-94^\circ$, suggesting a predominantly Galactic origin for anisotropies at these energies. The reconstructed dipole phases in the energy bins above 4 EeV point instead to right ascensions that are almost opposite to the Galactic center one, indicative of an extragalactic cosmic ray origin.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Green's functions applied to the theory of spectroscopy

Green’s functions are a powerful analytical and computational tool for ab initio calculations of X-ray spectra. For example, Green’s functions provide an efficient means for calculations over broad energy ranges since many-body effects can be incorporated naturally in terms of the electron self-energy. Here, their role in the theory of X-ray absorption and related spectroscopies is discussed, with particular focus on many-body effects such as quasi-particle energy shifts and lifetimes, core-hole interactions and electron–phonon interactions. Additionally, the cumulant expansion for the Green’s function is reviewed and compared with the usual GW Dyson equation approach for including multielectron excitations that produce satellite features in X-ray spectra.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Photoconversion in Disordered Semiconductors: Spatial, Spectral, and Temporal Insights through Nonlinear Microscopy (Final Technical Report)

Conversion of solar photons to useable energy requires two processes to occur successfully in a light-harvesting material: 1) the initial photoexcited state must efficiently produce excited states that are free to move, and 2) those states must be transported to a new location where they can do useful work before any potential is lost as heat. The core focus of this project examined the spatial and energetic dynamics of these two primary events of photoconversion in solution-processed semiconducting materials. The electronic properties of these systems are complex and strongly influenced by the cumulative interaction of local morphology and composition, along with defects, interfaces, and trap sites. It is therefore important to interrogate these systems at the local level, where structural and compositional heterogeneity can be precisely correlated to variation in functionality. Utilizing ultrafast laser spectroscopy coupled together with optical microscopy, this project examined how excited states and free charges spatially and energetically evolve on length scales between 10 nanometers and 10 microns and on timescales from 50 femtoseconds to 1 nanosecond. Insights gained from measurements made on these characteristic length and time scales provided new insight into how macroscopic functionality in disordered photovoltaic materials emerges from the interplay of nanoscale and mesoscale interactions.

14 SOLAR ENERGY↗

DOE OSTI2024

Californium-252 production at the High Flux Isotope Reactor - II: Comparison between the highly enriched uranium and a proposed low-enriched uranium core

This is the second paper on a 252 Cf production study performed in support of efforts to convert the High Flux Isotope Reactor (HFIR) from highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel. The first paper primarily focuses on validating computational tools and nuclear data. This companion paper evaluates another critical aspect: the 252 Cf production capability with a proposed LEU core. HFIR must maintain its world-class performance and missions following conversion and because 252 Cf is a vital, multipurpose neutron-emitting radioisotope, the ability to efficiently produce 252 Cf must be preserved. In this study, the HFIRCON transport and depletion tool, several nuclear data libraries, and Campaign 78 data were used to compute 252 Cf production, sensitivity, and safety metrics. Further, results indicate the 252 Cf production and production rates are slightly higher with a 95MW th LEU core compared with those obtained with the 85MW th HEU core. Additionally, the target peak fission rate densities, discharge cumulative fission densities, and heat deposition rates with the LEU core are within a few percent of those calculated with the HEU core. The findings suggest HFIR’s 252 Cf production capability can be effectively maintained with an LEU core without adversely affecting the safety metrics.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

actuated signal control↗

DOE OSTI2022

Ab initio calculation of X-ray and related core-level spectroscopies: Green's function approaches

X-Ray and related spectroscopies are powerful probes of atomic, vibrational, and electronic structure. In order to unlock the full potential of such experimental techniques, accurate and efficient theoretical and computational approaches are essential. Here we review the status of a variety of first-principles and nearly first principles techniques for X-ray spectroscopies such as X-ray absorption, X-ray emission, and X-ray photoemission, with a focus on Green's function based methods. In particular, we describe the current state of multiple scattering Green's function techniques available in the FEFF10 code and cumulant Green's function techniques for including the effects of many-body electronic excitations. Furthermore, illustrative examples are shown for a variety of materials and compared with other theoretical and experimental results.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Operation, Manufacturing, and Supply Chain of Lithium-ion Batteries for Electric Vehicles

The US Department of Energy (DOE) has a rich history of funding key innovations in batteries through multiple programs, including the Energy Efficiency and Renewable Energy programs sponsored by the Vehicle Technologies Office and Advanced Manufacturing Office. These DOE-sponsored innovations have been critical in lowering the cost of lithium-ion batteries to less than $160/kWh and increasing their energy density to more than 250 Wh/kg, enhancing the nation’s electrification system and achieving higher degrees of decarbonization. These investments continue to be crucial for positioning the United States as the hub for the most cumulative battery R&D programs worldwide. However, major breakthroughs in battery manufacturing are still needed to enable an electrified future evolving to predominantly incorporate rechargeable lithium-ion batteries. This will require a strong battery manufacturing base that will not only enable cutting-edge advancements in materials and manufacturing sciences but will also allow the United States to become a world leader in this fast-growing area. This review aims at supporting DOE’s Advanced Technology Vehicle Manufacturing Loan Program to give technology background and information that can be used to develop the basis for loan topics and evaluate battery applications. Such a program can represent a significant stride toward reinforcing the US position in advanced manufacturing by introducing artificial intelligence, machine learning, and sensing and smart controls to facilitate, expedite, and lead the battery industry by 2030.

25 ENERGY STORAGE↗

DOE OSTI2025

Aqueous-based recycling of perovskite photovoltaics

Abstract Cumulative silicon photovoltaic (PV) waste highlights the importance of considering waste recycling before the commercialization of emerging PV technologies 1,2 . Perovskite PVs are a promising next-generation technology 3 , in which recycling their end-of-life waste can reduce the toxic waste and retain resources 4,5 . Here we report a low-cost, green-solvent-based holistic recycling strategy to restore all valuable components from perovskite PV waste. We develop an efficient aqueous-based perovskite recycling approach that can also rejuvenate degraded perovskites. We further extend the scope of recycling to charge-transport layers, substrates, cover glasses and metal electrodes. After repeated degradation–recycling processes, the recycled devices show similar efficiency and stability compared with the fresh devices. Our holistic recycling strategy reduces by 96.6% resource depletion and by 68.8% human toxicity (cancer effects) impacts associated with perovskite PVs compared with the landfill treatment. With recycling, the levelized cost of electricity also decreases for both utility-scale and residential systems. This study highlights unique opportunities of perovskite PVs for holistic recycling and paves the way for a sustainable perovskite solar economy.

Science & Technology - Other Topics↗