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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Yes, No, Maybe So: Human Factors Considerations for Fostering Calibrated Trust in Foundation Models Under Uncertainty

High-stakes analytical environments require analysts to evaluate evidence and generate conclusions to inform critical decisions often under conditions of uncertainty. Probabilistic decision-making based on incomplete or inaccurate information can reduce productivity, compromise national interests, and endanger public safety. Researchers are developing expert systems built on foundation models (FMs) to support analysts’ decision-making processes by enabling human-artificial intelligence (AI) teaming, in part through the quantification and expression of uncertainty information. As FMs continue to mature, it is imperative to correspondingly consider analysts’ needs for appropriately interpreting and using uncertainty information. However, prior research indicates that it remains unclear how analysts engage with FM-generated uncertainty information and the extent to which these interactions influence trust in, and reliance on, expert systems. We plan to review the state of the science and conduct an exploratory, qualitative study to (a) understand how properly communicated uncertainty can foster calibrated trust and appropriate reliance and (b) identify approaches for effectively conveying FM-generated uncertainty information during analytical workflows. We will administer semi-structured interviews with analysts from a specific high-stakes analytical environment to collect their current experiences with job-related uncertainty and their impressions when viewing FM-generated uncertainty information. During the interview protocol, participants will be presented with several different FM outputs and invited to discuss their thoughts and beliefs about the uncertainty information displayed. Participants may provide insights into how trust and reliance may be influenced by uncertainty. The results of this study will help us to better understand how analysts currently interpret and use uncertainty information. Our findings may inform human factors recommendations for effectively conveying uncertainty information to foster calibrated trust in, and appropriate reliance on, expert systems. Interaction designers and FM developers can use this knowledge to enhance human-AI teaming and ensure the responsible deployment of FM-based expert systems in analytical workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Scalable foundation models for numerical simulations on HPC platforms

In recent years, foundation models (FMs) have begun to reshape numerical simulations on high-performance computing (HPC) platforms. These large, pre-trained AI models enable rapid predictions across a broad range of physical domains, including Earth system modeling, fluid dynamics, materials science, as well as complex multi-modal simulations in aerospace engineering and fusion research. By training on diverse datasets, FMs learn intricate relationships and underlying physical behavior while also enabling the quantification of uncertainty in their predictions. This capability allows simulations that once required days of numerical calculation to be completed in minutes (FM inference), supporting real-time design optimization, uncertainty-aware decision making, and more comprehensive exploration of complex scenarios.

AI↗
DOE OSTI2022

Elastic Solutions to 2D Plane Strain Problems: Nonlinear Contact and Settlement Analysis for Shallow Foundations

The classical Neumann boundary value problem of an isotropic, homogeneous elastic half-plane under plane strain conditions is readdressed as the limiting case of the fully three-dimensional problem. Analytical solutions of the stress and strain tensors are obtained by taking the limit from known three-dimensional solutions. It is shown that the displacement fields for the plane strain problem are not well defined. A small number of simple expressions are developed, which provide a general solution for linearly-varying traction over arbitrary regions on the boundary. A simple, efficient, and rapidly convergent algorithm is developed which uses these solutions as analytic elements and provides a solution approach to the general boundary value problem. The method is verified against known solutions for Hertzian contact between parallel cylinders. Two numerical examples are presented for the analysis of shallow foundation systems. In the first, the boundary conditions are informed by analytical elastoplastic calculations and a strain influence analysis is performed and compared with the Schmertmann method. Subsequently, empirical laboratory contact traction distributions measured by Bauer et al., in both the normal and tangential directions are employed as boundary conditions for an analysis of the underlying stress field.

42 ENGINEERING↗
NASA NTRSDate not supplied

Developing the Foundations of an Exploration Class Medical System: Bridging the Capability Gap between LEO and Mars

NASA's overarching mission is to drive advances in science, technology, aeronautics, and space exploration to enhance knowledge, education, innovation, economic vitality, and Earth stewardship. Its Artemis Program encompasses the next steps in human space exploration, critical elements of which include the Orion vehicle and Gateway outpost in lunar orbit. Artemis is the first "exploration class" space travel system with the stated mission to establish sustainable exploration of the lunar surface and serve as a bridge to future human exploration of Mars. Success requires understanding both the commonalities and the differences between our experience to date, predominantly in Low-Earth-Orbit, and the challenges expected in long-duration exploration missions. In addition, these missions will incur significant constraints on both vehicle and human systems. To that end, NASA Exploration Medical Capability (ExMC) element clinicians, engineers, and scientists are developing the "foundation" for a fully vehicle-integrated exploration-class medical system using model-based systems engineering to trace and leverage clinical and engineering content to expand and refine the fidelity and confidence in the medical system itself.

MEDICAL SYSTEMS↗
DOE OSTI2024

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including finetuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, finetuned FMs demonstrate competitive performance compared to base models trained from scratch.

commercial buildings↗
DOE OSTI2016

The spectroscopic foundation of radiative forcing of climate by carbon dioxide

The radiative forcing (RF) of carbon dioxide (CO 2 ) is the leading contribution to climate change from anthropogenic activities. Calculating CO 2 RF requires detailed knowledge of spectral line parameters for thousands of infrared absorption lines. A reliable spectroscopic characterization of CO 2 forcing is critical to scientific and policy assessments of present climate and climate change. Our results show that CO 2 RF in a variety of atmospheres is remarkably insensitive to known uncertainties in the three main CO 2 spectroscopic parameters: the line shapes, line strengths, and half widths. We specifically examine uncertainty in RF due to line mixing as this process is critical in determining line shapes in the far wings of CO 2 absorption lines. RF computed with a Voigt line shape is also examined. Overall, the spectroscopic uncertainty in present-day CO 2 RF is less than 1%, indicating a robust foundation in our understanding of how rising CO 2 warms the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multi-task Parallelism for Robust Pre-training of Graph Foundation Models on Multi-source, Multi-fidelity Atomistic Modeling Data

Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model’s transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generalizability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmutter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures.

Lupo Pasini, Massimiliano [ORNL] (ORCID:0000000249↗
DOE OSTI2017

Optimal prescribed burn frequency to manage foundation California perennial grass species and enhance native flora

Grasslands can be diverse assemblages of grasses and forbs but not much is known how perennial grass species management affects native plant diversity except in a few instances. We studied the use of late-spring prescribed burns over a span of 11 years where the perennial grass Poa secunda was the foundation species, with four additional years of measurements after the final burn. We also evaluated burn effects on P. secunda, the rare native annual forb Amsinckia grandiflora and local native and exotic species. Annual burning maintained P. secunda number, resulted in significant expansion, the lowest thatch and exotic grass cover, the highest percentage of bare ground, but also the lowest native forb and highest exotic forb cover. Burning approximately every 3 years maintained a lower number of P. secunda plants, allowed for expansion, and resulted in the highest native forb cover with a low exotic grass cover. Burning approximately every 5 years and the control (burned once from a wildfire) resulted in a decline in P. secunda number, the highest exotic grass and thatch cover and the lowest percentage of bare ground. P. secunda numbers were maintained up to 4 years after the final burn. And while local native forbs benefited from burning approximately every 3 years, planted A. grandiflora performed best in the control treatment. A. grandiflora did not occur naturally at the site; therefore, no seed bank was present to provide across-year protection from the effects of the burns. Thus, perennial grass species management must also consider other native species life history and phenology to enhance native flora diversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Comparing two methods for determining appropriateness of myocardial perfusion imaging: Criteria from the American College of Cardiology Foundation and the American College of Radiology

Background: Appropriate use criteria (AUC) developed by the American College of Cardiology Foundation and the appropriateness criteria (AC) developed by the American College of Radiology (ACR) are two existing methods of rating appropriateness of myocardial perfusion imaging (MPI). One study found poor agreement of ratings between the two methods. However, using the most contemporary AUC from 2013, it is unknown if poor agreement still exists. Methods: Retrospective cohort investigation comparing patients undergoing nuclear MPI between June 2011 and September 2014. The appropriateness category was determined based on the 2013 AUC (Appropriate, may be appropriate, rarely appropriate) and the 2010 ACR AC (usually appropriate, maybe appropriate, usually not appropriate). The primary outcome was the degree of the agreement between the two methods. Results: The kappa coefficient between ACR AC and AUC was 0.32, P < 0.0001, indicating poor agreement; 40 (8%) patients were classified by the AUC but could not be classified by the ACR AC. Conclusion: The two methods for rating the appropriateness of MPI have poor agreement; a potential for disagreement between providers and payers if only one method is used.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Editorial special issue: Advancing foundational sun-induced chlorophyll fluorescence science

The first research on the possibilities offered by chlorophyll-a fluorescence to track the daily course of CO 2 assimilation by leaves dates back to the last century (Kautsky and Hirsch, 1931). In the second half of the XXth Century, the development of the field of active fluorescence, which relies mainly on the pulse amplitude modulation (PAM) fluorimetry technique, allowed the unraveling of the relationship between the yield of chlorophyll-a fluorescence and photochemistry (linear electron transport), which is modulated by a third process known as non-photochemical quenching (Genty et al., 1989). Since then, active fluorescence has been regularly used in ecophysiology, forestry, and crop sciences to understand the plant response to stress (Schreiber, 2004). Since the beginning of the 2000s, the development of portable field spectrometers allows for measuring passive sun-induced fluorescence (SIF) at strong solar or telluric absorption features, which do not rely on the use of artificial excitation light (e.g., Meroni and Colombo, 2006; Meroni et al., 2009; Perez-Priego et al., 2005). Since then, the field has rapidly evolved, and now fluorescence is measurable with automated field spectrometers in the field (Grossmann et al., 2018; Gu et al., 2019b; Rossini et al., 2010), airborne platforms (Rascher et al., 2015; Zarco-Tejada et al., 2000), and satellites (Guanter et al., 2012; Sun et al., 2018). These advances offer the potential to couple ecosystem-scale measurements of CO 2 fluxes and SIF to probe new aspects related to ecosystem structural impacts on SIF andphotosynthesis processes at different time-scales and on different ecosystems (e.g., Damm et al., 2010; Magney et al., 2019; Porcar-Castell et al., 2021). While SIF has been proven as a good proxy of gross primary productivity (GPP), mainly across large spatial and temporal gradients, a series of studies showed that different factors could confound this relationship, namely different canopy structures (e.g., Dechant et al., 2020; Migliavacca et al., 2017), stress conditions (e.g., Martini et al., 2022; Wieneke et al., 2018; Wohlfahrt et al., 2018), nutritional conditions (Cendrero-Mateo et al., 2015; Martini et al., 2019), species-specific differences (e.g., Van Wittenberghe et al., 2013) and light regimes (e.g., Liu and Liu, 2018). At the same time, the modeling of SIF developed rapidly (Gu et al., 2019a; Han et al., 2022; Han et al., 2021; van der Tol et al., 2014; van der Tol et al., 2009) and with an increasing degree of realism, offering the possibility to retrieve important vegetation parameters from remote sensing (Pacheco-Labrador et al., 2019; Verrelst et al., 2015; Verrelst et al., 2016; Zhang et al., 2014). Excellent and comprehensive reviews of the field are by (Mohammed et al., 2019; Porcar-Castell et al., 2021; Porcar-Castell et al., 2014; Sun et al., 2023a; Sun et al., 2023b). Despite the exponential increase in the number of publications in the field, significant progress is still needed. Here, this special issue aims to report foundational SIF science, including theoretical modeling and measurement-based research that is urgently needed to unleash the full potential of SIF for physiological and ecological applications at scales spanning from leaf to globe. The 14 articles collected in this special issue reported on the following specific aspects: first, technical capabilities enabling spectrally resolved SIF observations and their inter-comparability in space and time; second, theoretical developments in SIF-photosynthesis relationships to correctly interpret the signal and extract mechanistic information on vegetation structure and function; third, evaluation of the potential of SIF to track process beyond photosynthesis, such as transpiration; fourth, large scale-applications of SIF observations; and finally, upscaling fluorescence from leaf to canopy scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dioctyl ether radiolysis under used nuclear fuel reprocessing conditions: Foundational knowledge for the development of sacrificial ligand grafts

Radiation-induced ligand destruction and concomitant degradation product formation is unavoidable under envisioned used nuclear fuel reprocessing conditions, and ultimately limits the recyclability of a given solvent system formulation. With this in mind, a novel approach to ligand innovation for advanced separations processes is in the design of tailored “protomolecules,” which when exposed to ionizing radiation undergo desired chemical transformations, thereby allowing for a reprocessing solvent system to advantageously evolve with absorbed radiation dose. Here we provide foundational knowledge for the time-resolved (electron pulse) and steady-state (cobalt-60 gamma) radiolytic behavior of dioctyl ether, a protomolecule grafting surrogate. Gamma irradiation of single-phase solutions of dioctyl ether (5–100 vol.%) in n-dodecane resulted in the loss of dioctyl ether (G = -0.72 µmol J –1 ) and the formation of octane (G = 0.11 µmol J –1 ) and octanol (G = 0.15 µmol J –1 ) degradation products, the latter of which is a known reprocessing phase modifier and radioprotectant. Further, these radiation-induced changes were attributed to direct radiation effects, for more concentrated dioctyl ether solutions, and indirect radiation effects, predominantly driven by the reaction of dioctyl ether with the dodecane radical cation, for which we report a second-order rate coefficient of k = (1.53 ± 0.05) × 10 10 L mol –1 s –1 . Under typical biphasic extraction (4.0 mol L –1 HNO 3 ) and strip (0.1 mol L –1 HNO 3 ) reprocessing conditions, gamma irradiation of 5 vol.% dioctyl ether solvent systems afforded negligible change in the rate of parent molecule destruction but promoted significant differences in the radiolytic behavior of its degradation products. These differences are attributed to their respective interactions with [dioctyl ether/HNO 3 /H 2 O] and [octanol/HNO 3 /H 2 O] adducts extracted into the organic phase. Overall, these results support the grafting of ether linkages to advanced separations ligands (e.g., modified diglycolamides).

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Engineering the S-Layer of Caulobacter crescentus as a Foundation for Stable, High-Density, 2D Living Materials

Materials synthesized by organisms, such as bones and wood, combine the ability to self-repair with remarkable mechanical properties. This multifunctionality arises from the presence of living cells within the material and hierarchical assembly of different components across nanometer to micron scales. While creating engineered analogs of these natural materials is of growing interest, our ability to hierarchically order materials using living cells largely relies on engineered 1D protein filaments. Here, we lay the foundations for bottom-up assembly of engineered living material composites in 2D along the cell body using a synthetic biology approach. We engineer the paracrystalline surface-layer (S-layer) of Caulobacter crescentus to display SpyTag peptides that form irreversible isopeptide bonds to SpyCatcher-modified proteins, nanocrystals, and biopolymers on the extracellular surface. Using flow cytometry and confocal microscopy, we show that attachment of these materials to the cell surface is uniform, specific, and covalent, and its density can be controlled based on the location of the insertion within the S-layer protein, RsaA. Furthermore, we leverage the irreversible nature of this attachment to demonstrate via SDS-PAGE that the engineered S-layer can display a high density of materials, reaching 1 attachment site per 288 nm 2 . Lastly, we show that ligation of quantum dots to the cell surface does not impair cell viability and this composite material remains intact over a period of two weeks. Taken together, this work provides a platform for self-organization of soft and hard nanomaterials on a cell surface with precise control over 2D density, composition, and stability of the resulting composite, and is a key step towards building hierarchically-ordered engineered living materials with emergent properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Cross-functional transferability in foundation machine learning interatomic potentials

The rapid development of foundation potentials (FPs) in machine learning interatomic potentials demonstrates the possibility for generalizable learning of the universal potential energy surface. The accuracy of FPs can be further improved by bridging the model from lower-fidelity datasets to high-fidelity ones. In this work, we analyze the challenge of this transfer learning (TL) problem within the CHGNet framework. We show that significant energy scale shifts and poor correlations between GGA and r 2 SCAN hinder cross-functional transferability. By benchmarking different TL approaches on the MP-r 2 SCAN dataset, we demonstrate the importance of elemental energy referencing in the TL of FPs. By comparing the scaling law with and without the pre-training on a low-fidelity dataset, we show that significant data efficiency can still be achieved through TL, even with a target dataset of sub-million structures. We highlight the importance of proper TL and multi-fidelity learning in creating next-generation FPs on high-fidelity data.

Huang, Xu [University of California, Berkeley, CA ↗