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DOE OSTI2026

Status Update of Permanent Magnet Radiation Resiliency Studies at CEBAF

The proposed energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) incorporates Fixed-Field Alternating-gradient (FFA) arcs utilizing permanent magnet technology. Given the radiation environment within the CEBAF tunnel enclosure, validating the long-term magnetic stability of these materials is a critical step for the project's technical feasibility. This contribution presents an overview of the ongoing permanent magnet radiation resiliency program at Jefferson Lab. We briefly review the experimental methodology used to monitor demagnetization in situ and summarize the operational experience from the initial data-taking campaign. Furthermore, we discuss the upgrades implemented for the second exposure campaign, currently underway, which aims to refine dose correlation and reduce systematic uncertainties. We report on the general status of the program and the roadmap for certifying permanent magnet optics for the proposed upgrade energies.

Bodenstein, R. [Thomas Jefferson National Accelera↗

DOE OSTI2024

Decoherence Noise on the Superconducting Qubits Training Program

Quantum computing is a growing field with promising applications in a variety of fields such as healthcare, energy consumption, and cryptography. Quantum computing leverages the principles of quantum mechanics - superposition and entanglement. Yet, in the Noisy Intermediate Scale Quantum (NISQ) Era - quantum systems face the major challenge of decoherence due to noise. This era is characterized by low amounts of qubits and high gate error. Decoherence leads to the loss of the quantum information stored in the qubit. Noise occurs with any quantum system that is exposed to the environment. It should also be noted that quantum information can be stored in the cavity - Fermilab specializes in coupling transmons to ultrahigh-Q SRF cavities. The Superconducting Qubits Training Program (SQTP) provides a visualization for beginners in quantum computing. The open quantum system simulated is a superconducting qubit (two-level atom) coupled to a microwave cavity whose excitations are photons. The Rotating Wave Approximation of the Jaynes-Cumming Hamiltonian is used. SQTP utilizes open-source Python-based libraries scQubits, NumPy, and QuTiP alongside the Master Lindblad equation. In this project, we study the different decay behaviors of qubits and cavities with collapse operators.

Lopez, Sara↗

DOE OSTI2024

Energy Auditor and Quality Control Inspector Certification Updates

This presentation discusses recent and future Home Energy Professional credential and resource updates at the National Renewable Energy Laboratory in support to the U.S Department of Energy's Weatherization Assistance Program (WAP). It will provide an in-depth overview of planned improvements to the EA and QCI certification schemes.

certification↗

DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗

DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Determining Stability Margins in Adiabatic Superconducting Magnets with 3-D Finite Element Analysis

Superconducting magnets play a key role in the development of experiments at Fermilab; understanding the operating stability of these can allow us to utilize more potent magnets for future experiments (like the proposed Muon Collider), optimize the design of magnets in more immediate experiments (like Mu2e), and research the future use of more exotic materials (like high-temperature superconductors). This summer, I developed a 3-D parametric FEA program in ANSYS Mechanical APDL that simulates quench in superconducting magnets, and I also developed a parametric MATLAB program that predicts thermal behavior in magnet quench using the MIITS method. These programs can provide useful quenching parameters (like minimum quench energy and normal zone propagation velocity) for different cases of quench, leading to the previously mentioned objective of magnet design optimization. To test the programs, preliminary cases were run and the data produced was compared and analyzed. The results of these analyses, as well as the program operating methods, are discussed in this project.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Exploiting Modern C++ for Portable Parallel Programming in Lattice QCD Applications

The evolution of ISO C++ standards increasingly serves the needs of scientific computing, offering potential benefits for developing portable applications. The recent revisions of C++ programming language, for instance, introduces a suite of algorithms capable of being executed on accelerators. Although this approach may not yield best performance, it can present a viable balance between code productivity and computational efficiency. In this report, we discuss the implementation of the HISQ operator utilizing a range of features from the C++17/20/23 standards and include an assessment of their performance.

Strelchenko, Alexei↗

DOE OSTI2025

Energy Star Portfolio Manager (ESPM) Training [Slides]

This is a presentation developed by the NREL-Alaska team for the Nonprofit Retrofits for Health and Housing (NORTHH) Program. This presentation will be shared with nonprofits and community members in and around the Interior Alaska region to share information about Energy Star Portfolio Manager (ESPM). ESPM is used to track the energy performance of buildings in the United States and was used on the NORTHH Project to understand the performance of nonprofit owned buildings in the program. This presentation will allow users to learn how to leverage the benefits of this tool.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Energy Efficiency Analysis for Residents of North Birmingham, Alabama

The community of North Birmingham, Alabama, participated in the Communities LEAP pilot program. As part of this technical assistance pilot, residential buildings, energy efficiency, and electrification were analyzed. This factsheet has a summary of the results.

building envelope↗

DOE OSTI2025

Emerging Tools to Support DILI Assessment in Clinical Trials with Abnormal Baseline Serum Liver Tests or Pre-existing Liver Diseases

Abstract Based on the late Dr. Hyman Zimmerman’s observation that hepatocellular drug-induced liver injury (DILI) leading to jaundice carries a ≥ 10% fatality risk (coined as Hy’s law by others), evaluation of Drug-Induced Serious Hepatotoxicity (eDISH) continues to play a central role in the assessment of a study drug’s liability for acute hepatocellular DILI. The eDISH identifies drugs in clinical trials with DILI fatality (death or transplant) risk that may be unacceptable in a post-market setting. As a two-dimensional graph that plots peak total bilirubin (TB) versus peak serum aminotransferase levels for each patient during study drug or comparator treatment, eDISH identifies potential cases of acute, modest, and serious hepatocellular DILI for in-depth analysis of liver tests (LT) and clinical course so that the likelihood of causal association with the study drug can be determined. Unfortunately, the generalizable utility of this tool only pertains to trials enrolling patients with normal or near normal (NNN) baseline (BL) serum LTs. The eDISH does not necessarily apply to trials of patients with abnormal baseline (ABN-BL) LTs that often coincide with underlying liver disorders. Because drug development programs being reviewed by the FDA increasingly target liver disorders, we are often challenged to evaluate DILI risk in trials of patients with ABN-BL LTs. Also, the high background prevalence of metabolic dysfunction associated steatotic liver disease (MASLD) means patients with LTs above NNN may need to be enrolled in trials treating non-liver disorders to reflect the target population. Such study populations create challenges for industry and regulators because eDISH may not reliably categorize or identify potential cases of DILI for further analysis, as it so efficiently does in NNN-BL trials. We describe the main functionalities of eDISH in NNN-BL trials to understand what should be emulated by new tools or eDISH modifications. We then discuss non-eDISH–based plots that may be useful in ABN-BL trials.

Amirzadegan, Jasmine↗

DOE OSTI2024

Automated Calibration System for Beam Current Monitor

The Beam Current Monitor (BCM) measures the instantaneous current in a beam. This work aims to develop an automated calibration system for the BCM to address the need for calibration without interrupting beam operation. We strive to create a synchronous calibration method integrated into the master timeline by utilizing the pulsed nature of synchrotrons to run calibration pulses during inter-pulse gaps. This improvement should enhance operational efficiency, ensure safe operation, and help mitigate beam loss, all by enabling intermittent calibration during the operation of the accelerator. This is accomplished by developing a Python program that interfaces with a Keithley 6221 DC and AC source and a Keithley 2182A Nanovoltmeter. The program configures both of these devices to perform a selected mode of the current sweep, allows for the initiation of the current sweep, collects the measured voltage data from the 2182A, stores the collected data to the computer in a CSV file, and graphs the collected data.

Haller, James↗

DOE OSTI2025

CCQE-like $\nu_{e}$ Selection in SBND using Convolutional Visual Network

Neutrinos from the Booster Neutrino Beam (BNB) at Fermilab interact with argon in a Liquid Argon Time Projection Chamber (LArTPC) differently based on their flavour. By examining the particles produced in a charged-current interaction, both the interaction type and the neutrino flavour can be inferred. The Short Baseline Near Detector has the largest neutrino-argon cross section data to date, motivating in-depth studies of various cross-section channels and topologies. This project aims to select electron neutrino quasi-elastic-like (QE-like) interactions in SBND using Convolutional Visual Network (CVN) scores. The CVN is a neural network that processes visual information from an event and assigns scores corresponding to its likelihood of being each interaction type. An inclusive study of electron neutrino charged current interactions using CVN has already been conducted. This analysis aims to build on this study, further utilizing CVN scores to isolate electron neutrino QE-like interactions characterized by the presence of an electron and one or more protons ($N > 0$) in the final state. The project s goal is to contribute to the overall cross-section measurement efforts within the SBN program at Fermilab.

Breen, Genevieve [Mt. Holyoke Coll.]↗

DOE OSTI2025

Benefits of Dual Fuel Heat Pump Grid-responsive Control: A Model-based Control Optimization Approach Using Building and Equipment Co-simulation

Conventional dual fuel heat pumps lack the intelligent control mechanisms to efficiently manage the switch between heat pump and furnace, leading to sub-optimal energy usage and, in some cases, increased operating costs. To resolve this gap, this study applies optimized control on hybrid heat pumps. With a focus on equipment control strategies, we compare the performances of five spacing heating equipment, including a conventional heat pump (HP), a conventional furnace, a dual fuel heat pump (DFHP) with conventional control, a dual fuel heat pump with smart control, and a novel seamlessly fuel flexible heat pump (SFFHP). While DFHP runs on either gas or electricity at any given moment, SFFHP concurrently consumes gas and electricity by continuously optimizing the proportion of each. In this research, a co-simulation framework is developed by integrating a building envelope model with a physics-based heat pump simulation model to analyze the benefits of grid-responsive controls of DFHP and SFFHP. The model-based optimal controls adjust the operation of the heat pump and gas furnace based on utility price signals and marginal grid emission to minimize utility cost and CO 2 emissions for multiple climate zones, different utility tariffs, and marginal grid emission scenarios. Case studies in Chicago and Los Angeles demonstrate that SFFHP and DFHP, with model-based optimal control, can deliver significant reductions in peak demand, utility cost, and CO 2 emission. In Chicago, SFFHP and smart controlled DFHP yield up to 64.7% and 61.7% utility cost reduction and up to 15.7% and 8.5% CO 2 emission reduction compared to the gas furnace. In Los Angeles, SFFHP and smart controlled DFHP achieve up to 43.6% and 40.1% utility cost reduction and up to 13.8% and 14.1% CO2 emission reduction compared to conventional heat pumps. In conclusion, by leveraging the fuel flexibility nature of dual fuel heat pumps, the model-based control optimization approach makes dual fuel heat pump an attractive option for demand response programs.

Control↗

DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗

DOE OSTI2024

Coassembly and binning of a twenty-year metagenomic time-series from Lake Mendota

Abstract The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

2009 National Household Travel Survey - California Add-On

The California add-on survey supplements the 2009 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The NHTS was conducted by the Federal Highway Administration, which collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The California Department of Transportation participated in the NHTS add-on program and received a total of 21,225 household samples in California, from March 2008 through May 2009.

1Hz data↗

DOE OSTI2026

2017 National Household Travel Survey - California Add-On

The California add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The NHTS was conducted by the Federal Highway Administration, which collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The California Department of Transportation participated in the NHTS add-on program and received a total of 26,095 household samples in California, from April 2016 through April 2017.

1Hz data↗

DOE OSTI2026

Electrify Monterey Bay

The Electrify Monterey Bay project, delivered by Central Coast Energy Services (CCES) under DOE Weatherization Assistance Program Enhancement & Innovation (E&I) funding, was designed to demonstrate scalable, low-income residential electrification across the Monterey Bay region over a 36-month performance period. The original production goal was to electrify at least 50 households; CCES exceeded this benchmark by completing electrification work in 86 households. Targeting criteria prioritized households located in disadvantaged communities and/or with an energy burden >6.5% (defined as average household energy costs divided by average household countable income). Across participating households, the average energy burden was 28%, indicating a high-need population and supporting the program’s equity and affordability objectives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗