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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

NISQ Benchmarking

Test suite of quantum algorithms for Noisy Intermediate Scale Quantum (NISQ) computers. The test suite includes benchmark-style code for quantum volume circuits (QV), fairness sampling circuits, quantum telecloning circuits, and other NISQ benchmark style algorithms on small problems (i.e., up to 100 qubits), such as Variational Quantum Eigensolver (VQE), Hamiltonian Simulation, and Grover unstructured search example circuits. These benchmark-style applications are implemented in quantum software packages, mostly IBM's QISKIT, but may include vendor-specific frameworks, such as PyQuil (for Rigetti) or Q\# for Microsoft, or CirQ (for Google) as the test suite grows with the vendor sample. The test suite also includes numerical simulation code for Quantum Alternating Operator Ansatz (QAOA) algorithms, VQE, Hamiltonian Simulation and search examples. Numerical simulation code simulates quantum computers on classical computers, which is only possible for small problem instances; the implementation framework of choice is typically within Python, using the numpy/scipy libraries as well as extensions to the Julia language.

Pelofske, Elijah↗
DOE OSTI2024

TICC Clustering Library v.1.0

SAND2024-01234O TICC is a clustering algorithm that labels a sequence of data points according to numerical properties. This library is a Python implementation of the algorithm described in "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data" (Hallac et al. 2017). It includes documentation, performance improvements, examples, and test coverage. This library allows users to automatically segment a series of multivariate data points according to their covariance—that is, the way the values at each data point are changing in relation to one another. This is useful for identifying periods in which a system is behaving. For example, if a sensor is measuring a car's velocity, steering wheel angle, braking and acceleration, TICC can determine when the car was stopped, beginning/exiting a turn, slowing or accelerating at an intersection, or driving on straight or curved roads. TICC can be applied to measure multiple quantities at known times. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Dalbey, Keith↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2025

MasQiTT

SAND2025-03471O MasQiTT software is a proof-of-concept code that implements the Secure MQTT protocol. It demonstrates a publish/subscribe model of message passing within a network where the publishers can encrypt messages according to a topic and the subscribers can decrypt those messages. The example code shows an example of deployment of an end-to-end system that includes certificate authority and key management in addition to basic implementation of the protocol. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fisher, Andrew [Sandia National Lab. (SNL-CA), Liv↗
DOE OSTI2025

HydraGNN v4.0

The new version of HydraGNN v4.0 provides additional core capabilities, such as: Inclusion of multi-body atomistic cluster expansion MACE, polarizable atom interaction neural network PAINN, and equivariant principal neighborhood aggregation (PNAEq) among the message passing layers supported -Inclusion of graph transformers to directly model long-range interactions between nodes that are distant in the graph topology Integration of graph transformers with message passing layers by combining the graph embedding generated by the two mechanisms, which allows for an improved expressivity of the HydraGNN architecture Improved re-implementation of multi-task learning (MTL) to allow its use for stabilized training across imbalanced, multi-source, multi-fidelity data Introduction of multi-task parallelism, a newly proposed type of model parallelism specifically for MTL architectures, which allows to dispatch different output decoding heads to different GPU devices Integration of multi-task parallelism with pre-existing distributed data parallelism to enable a 2D parallelization for distributed training Improved portability of the distributed training across Intel GPUs, which has been testes on ALCF exascale supercomputer Aurora Inclusion of 2-level fine-grained energy profilers portable across NVIDIA, AMD, and Intel GPUs to monitor the power and energy consumption associated with different functions executed by the HydraGNN code during data pre-load and training Restructuring of previous examples and inclusion of new sets of examples to illustrate the download, preprocess, and training of HydraGNN models on new large-scale open-source datasets for atomistic materials modeling (e.g., Alexandria, Transition1x, OMat24, OMol25)

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2025

HALLUFIELD: DETECTING LLM HALLUCINATIONS VIA FIELD-THEORETIC MODELING

A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.

Bhattarai, Manish [Los Alamos National Labs]↗
DOE OSTI2021

The Shortwave Spectral Radiometer for Atmospheric Science: Capabilities and Applications from the ARM User Facility

Industry advances have greatly reduced the cost and size of ground-based shortwave (SW) sensors for the ultraviolet, visible, and near-infrared spectral ranges that make up the solar spectrum, while simultaneously increasing their ruggedness, reliability, and calibration accuracy needed for outdoor operation. These sensors and collocated meteorological equipment are an important part of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility, which has supported parallel integrated measurements of atmospheric and surface properties for more than two decades at fixed and mobile sites around the world. The versatile capability of these ground-based measurements includes 1) rich spectral information required for retrieving cloud and aerosol microphysical properties, such as cloud phase, cloud particle size, and aerosol size distributions, and 2) high temporal resolution needed for capturing fast evolution of cloud microphysical properties in response to rapid changes in meteorological conditions. Here we describe examples of how ARM's spectral radiation measurements are being used to improve understanding of the complex processes governing microphysical, optical, and radiative properties of clouds and aerosol.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗
DOE OSTI2022

Single reader between-cases AUC estimator with nested data

The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.

Health Care Sciences & Services↗
DOE OSTI2026

Finite deformation implementation of a mixed-mode single-integral type cohesive zone with reorienting surfaces of separation

To model material ductile failure and crack propagation, cohesive zone elements can be embedded along potential fracture paths in a finite element simulation. When damage criteria are met, elements in the mesh decohere, simulating the formation and propagation of a crack. In this paper, we present a novel computational algorithm based on finite deformation theory, essential to modeling crack initiation and growth in solids undergoing large deformations. This new algorithm was formulated within a Lagrangian frame of reference to extend previous cohesive zone algorithms to include modeling crack growth in finite deformation contexts. The local coordinate system, necessary for defining an embedded cohesive zone, is constructed based upon the current configuration and is updated within the nonlinear iteration process, thereby resulting in the convergence of the solution for a growing crack in a large deformation quasi-static setting. The model’s accuracy was demonstrated by comparing finite element model simulation results with the analytic case of a constant surface separation, as shown in the verification examples. The power and efficacy of the algorithm to capture large deformations during crack growth were then demonstrated with a double cantilever beam example case. It indicates that the model can be applied to a variety of physical circumstances for predicting crack initiation and growth with delamination and fracture.

42 ENGINEERING↗
DOE OSTI2022

A new dynamic zOnal model with air-diffuser (DOMA) - Application to thermal comfort prediction

A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m 2 , would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.

Construction & Building Technology↗
DOE OSTI2023

Pickaxe: a Python library for the prediction of novel metabolic reactions

Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13 >C and 1 H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13 C and 1 H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1 H and 13 C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Improving Dose-Optimization Processes Used in Oncology Drug Development to Minimize Toxicity and Maximize Benefit to Patients

This review highlights strategies to integrate dose optimization into premarketing drug development and discusses the underlying statistical principles. Poor dose optimization can have negative consequences for patients, most commonly because of toxicity, including poor quality of life, reduced effectiveness because of inability of patients to stay on current therapy or receive subsequent therapy because of toxicities, and difficulty in developing combination regimens. We reviewed US Food and Drug Administration initial approvals (2019-2021) of small molecules and antibody-drug conjugates for oncologic indications to determine the proportion with a recommended dosage at the maximum tolerated dose or the maximal administered dose, to characterize the use of randomized evaluations of multiple dosages in dose selection, to describe the frequency of dose modifications at the recommended dosage, and to identify case examples that highlight key principles for premarket dose optimization during drug development. Herein, we highlight major principles for dose optimization and review examples of recent US Food and Drug Administration approvals that illustrate how investigation of dose- and exposure-response relationships and use of randomized dose trials can support dose optimization. Although there has been some progress, dose optimization through randomized dose evaluation in oncology trials is not routinely conducted. Dose optimization is essential to ensure that patients receive therapies which maximize efficacy while minimizing toxicity.

Oncology↗
DOE OSTI2023

Chapter 13: All-Electric Vertical Take-Off and Landing Aircraft (eVTOL) for Sustainable Urban Travel

Increasing urbanization, ground congestion, and greenhouse gas emissions have spurred aircraft pioneers to capitalize on advancements in battery technology, electric motors, software, and other advances to develop all-electric vertical take-off and landing (eVTOL) aircraft intended to save travelers time within densely populated urban regions. This study served as an example for conducting a life-cycle analysis (LCA) of an eVTOL aircraft, analyzing its manufacturing process, its in-life operation as a passenger transportation service between urban locations, and (in part) end-of-life treatment. This case study aims to provide guidance on how to accurately conduct eVTOL LCA, particularly on how to define the problem, how to obtain the right information from engineers inside and outside their own organizations, and what areas to focus on. As LCA results are only as accurate as the assumptions that were made, a more rigorous LCA that includes sensitivity analysis and uncertainty characterization can help further understand the limitations of those assumptions. New technologies such as blockchain may also play an important role in LCA, including the life-cycle inventory and improving confidence intervals. Completing a credible LCA is central to supporting the argument in favor of introducing the new technology. Completing such a study will allow companies to demonstrate to relevant stakeholders the likely impact of their aircraft on the environment by quantifying the operational climate footprint of the aircraft and the manufacturing process used to build it. To this end, this case study also provides examples of how to report and present LCA results, optimizing for communications to non-LCA experts. The LCA can also assist in identifying opportunities to improve the environmental performance at various life-cycle stages, informing decision makers as products and manufacturing processes evolve or are redesigned. Given the nascency of both eVTOL aircraft manufacturing and operations, it is strongly recommended that LCA analysts treat reports on eVTOL aircraft as works-in-progress. As new information is found, design changes are made, and the aircraft or operation gains maturity, it is inevitable that some amendments will need to be made to the LCA. It is suggested that a complete overhaul of the LCA be made every 18-24 months throughout the R&D, design, and operational scale-up phases of the project.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Developing a remediation framework

When harmful contaminants are detected in soil, sediment, or groundwater (or suspected due to industrial activities), remediation efforts may be required to protect human health and the environment. This chapter will introduce and discuss the steps in the general site remediation process that are key when approaching any remediation strategy, regardless of the site complexity. While these steps are described in a linear fashion, in most cases, the approach is more fluid and iterative depending on the desired outcomes and success of each progressive clean-up effort along the way. This chapter will additionally articulate some key considerations which may be important to the overall remediation strategy depending on the particular needs and desired outcomes. From these more general discussions, the chapter will branch into a case-study of the remediation approach, or process steps, previously/currently/in the future taking place at the Hanford Site, as one example of a complex waste sites in the United States. As indicated earlier, while a remediation strategy can be more simply described by a progression of general process steps, it is far more likely to observe fluidity and strategy evolution over time from the first conceived strategy as remediation progress occurs. The chapter concludes with a brief discussion of how the strategy at the Hanford Site has evolved to provide a singular example of what this evolution might look like for other complex waste sites.

Muller, Katherine A.↗
DOE OSTI2024

Tank Waste Characterization: History, Challenges, and Success Stories

The preparation and chemical and radiochemical analysis of Hanford tank waste samples can be performed with standard laboratory equipment and instruments as relatively routine processes that are not particularly challenging. Rather, the main challenges of tank waste characterization are associated with radiological dose and sampling limitations. Accurate, representative and effective sampling techniques are difficult with the waste tanks because they were not designed for routine sampling. There are a finite number of sampling locations for each tank based on riser positioning, depth and the operational functionality of the sampling riser. For example, in one recently emptied SST, there was one riser that was found to have had concrete dumped down it, thereby eliminating that sampling port. Additionally, the waste within the tank; especially true for the saltcake and sludge, is not homogenous. The ability to adequately mix a million-gallon double shell tank (DST) is a concern for data reproducibility. Another real challenge that must be addressed for sampling single shell tanks, is how to dissolve the salt cake waste in a compromised (leaking) SST. These physical constraints mean that uncertainty in the representativeness of samples must be considered when applying analytical results to the bulk contents of the tank. The tank waste is highly radioactive and thus can only be handled initially by facilities that can receive samples into concrete-shielded hot cells with remote operation with an example provided in Figure 1. The shielding protects the worker from the radiological dose while mineral oil windows and remotely operated manipulators enables the samples to be handled. At Hanford, analytical laboratories with these hot cell capabilities are limited to the Pacific Northwest National Laboratory and the main Hanford operations support laboratory, 222-S Laboratory. Because of their highly radioactive nature, samples must be sufficiently diluted to facilitate their analysis outside of a shielded cell. In some cases, this means some accuracy must be compromised to complete the analysis beyond that normally encountered for non-radioactive material.

Waste Characterization, BBI, PHOENIX: Tank Farms: ↗
DOE OSTI2021

Explainable Artificial Intelligence in Endocrinological Medical Research

Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems (CDSS) to aide physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to the integration of machine learning (ML) algorithms to drive the output of these AI systems due to the ability of ML models to more accurately make predictions by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capture complex and often non-linear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the clinical utility of often black-box solutions driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; for example a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5).

Endocrinological, artificial intelligence, diabete↗